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Abstract Imaging-based spatial transcriptomics technologies such as Multiplexed error-robust fluorescence in situ hybridization (MERFISH) can capture cellular processes in unparalleled detail. However, rigorous and robust analytical tools are needed to unlock their full potential for discovering subcellular biological patterns. We present Intracellular Spatial Transcriptomic Analysis Toolkit (InSTAnT), a computational toolkit for extracting molecular relationships from spatial transcriptomics data at single molecule resolution. InSTAnT employs specialized statistical tests and algorithms to detect gene pairs and modules exhibiting intriguing patterns of co-localization, both within individual cells and across the cellular landscape. We showcase the toolkit on five different datasets representing two different cell lines, two brain structures, two species, and three different technologies. We perform rigorous statistical assessment of discovered co-localization patterns, find supporting evidence from databases and RNA interactions, and identify associated subcellular domains. We uncover several cell type and region-specific gene co-localizations within the brain. Intra-cellular spatial patterns discovered by InSTAnT mirror diverse molecular relationships, including RNA interactions and shared sub-cellular localization or function, providing a rich compendium of testable hypotheses regarding molecular functions.

Abstract Estrogen Receptor α (ERα) is a major lineage determining transcription factor (TF) in mammary gland development. Dysregulation of ERα-mediated transcriptional program results in cancer. Transcriptomic and epigenomic profiling of breast cancer cell lines has revealed large numbers of enhancers involved in this regulatory program, but how these enhancers encode function in their sequence remains poorly understood. A subset of ERα-bound enhancers are transcribed into short bidirectional RNA (enhancer RNA or eRNA), and this property is believed to be a reliable marker of active enhancers. We therefore analyze thousands of ERα-bound enhancers and build quantitative, mechanism-aware models to discriminate eRNAs from non-transcribing enhancers based on their sequence. Our thermodynamics-based models provide insights into the roles of specific TFs in ERα-mediated transcriptional program, many of which are supported by the literature. We use in silico perturbations to predict TF-enhancer regulatory relationships and integrate these findings with experimentally determined enhancer-promoter interactions to construct a gene regulatory network. We also demonstrate that the model can prioritize breast cancer-related sequence variants while providing mechanistic explanations for their function. Finally, we experimentally validate the model-proposed mechanisms underlying three such variants.

Summary: Mesenchymal stem/stromal cell (MSC) therapies have had limited success so far in clinical trials due in part to heterogeneity in immune-responsive phenotypes. Therefore, techniques to characterize these properties of MSCs are needed during biomanufacturing. Imaging cell shape, or morphology, has been found to be associated with MSC immune responsivity—but a direct relationship between single-cell morphology and function has not been established. We used label-free differential phase contrast imaging and matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) to evaluate single-cell morphology and explore relationships with lipid metabolic immune response. In interferon gamma (IFN-γ)-stimulated MSCs, we found higher lipid abundances from the ceramide-1-phosphate (C1P), phosphatidylcholine (PC), LysoPC, and triglyceride (TAG) families that are involved in cell immune function. Furthermore, we identified differences in lipid signatures in morphologically defined MSC subpopulations. The use of single-cell optical imaging coupled with single-cell spatial lipidomics could assist in optimizing the MSC production process and improve mechanistic understanding of manufacturing process effects on MSC immune activity and heterogeneity.

Long non-coding RNAs (lncRNAs) have received attention in recent years for their regulatory roles in diverse biological contexts including cancer, yet large gaps remain in our understanding of their mechanisms and global maps of their targets. In this work, we investigated a basic unanswered question of lncRNA systems biology: to what extent can gene expression variation across individuals be attributed to lncRNA-driven regulation? To answer this, we analyzed RNA-seq data from a cohort of breast cancer patients, explaining each gene's expression variation using a small set of automatically selected lncRNA regulators. A key aspect of this analysis is that it accounts for confounding effects of transcription factors (TFs) as common regulators of a lncRNA-mRNA pair, to enrich the explained gene expression for lncRNA-mediated regulation. We found that for 16% of analyzed genes, lncRNAs can explain more than 20% of expression variation. We observed 25-50% of the putative regulator lncRNAs to be in 'cis' to, i.e., overlapping or located proximally to the target gene. This led us to quantify the global regulatory impact of such cis-located lncRNAs, which was found to be substantially greater than that of trans-located lncRNAs. Additionally, by including statistical interaction terms involving lncRNA-protein pairs as predictors in our regression models, we identified cases where a lncRNA's regulatory effect depends on the presence of a TF or RNA-binding protein. Finally, we created a high-confidence lncRNA-gene regulatory network whose edges are supported by co-expression as well as a plausible mechanism such as cis-action, protein scaffolding or competing endogenous RNAs. Our work is a first attempt to quantify the extent of gene expression control exerted globally by lncRNAs, especially those located proximally to their regulatory targets, in a specific biological (breast cancer) context. It also marks a first step towards systematic reconstruction of lncRNA regulatory networks, going beyond the current paradigm of co-expression networks, and motivates future analyses assessing the generalizability of our findings to additional biological contexts.

Millimeter-wave and Terahertz communications consist of complex analog and mixed-signal transceivers where overall system performance is often limited by the weakest performing subsystem. While analog and mixed-signal integrated circuits have significantly advanced, the future of 5G and 6G transceiver design could be accelerated by including artificial intelligence. In this combination, analog integrated circuit design and operation would harness machine learning to identify, characterize, and act upon variations and anomalies in system performance. Focusing on 5G and 6G, this paper investigates solutions for a unified intelligent integrated transceiver: a conceptual combination of a traditional analog subsystem, a supporting digital subsystem that enables artificial intelligence, and dedicated feedback circuitry or sensors that monitor performance, efficiency, or reliability. Active and passive components and propagation channels are reviewed based on their merits of introducing intelligence. Holistically and for broader applicability, the paper conceptualizes and coins the notion of an “intelligent integrated system (IIS)”, which brings forward a novel unified vision and approach toward context-aware subsystems that dynamically interact with ambient and varying operating conditions. To demonstrate viability, the paper concatenates a select set of measurement results.

Abstract Background Recent data suggest that myelin may be altered by physiological events occurring outside of the central nervous system, which may cause changes to cognition and behavior. Similarly, peripheral infection by non-neurotropic viruses is also known to evoke changes to cognition and behavior. Methods Mice were inoculated with saline or influenza A virus. Bulk RNA-seq, lipidomics, RT-qPCR, flow cytometry, immunostaining, and western blots were used to determine the effect of infection on OL viability, protein expression and changes to the lipidome. To determine if microglia mediated infection-induced changes to OL homeostasis, mice were treated with GW2580, an inhibitor of microglia activation. Additionally, conditioned medium experiments using primary glial cell cultures were also used to test whether secreted factors from microglia could suppress OL gene expression. Results Transcriptomic and RT-qPCR analyses revealed temporal downregulation of OL-specific transcripts with concurrent upregulation of markers characteristic of cellular stress. OLs isolated from infected mice had reduced cellular expression of myelin proteins compared with those from saline-inoculated controls. In contrast, the expression of these proteins within myelin was not different between groups. Similarly, histological and immunoblotting analysis performed on various brain regions indicated that infection did not alter OL viability, but increased expression of a cellular stress marker. Shot-gun lipidomic analysis revealed that infection altered the lipid profile within the prefrontal cortex as well as in purified brain myelin and that these changes persisted after recovery from infection. Treatment with GW2580 during infection suppressed the expression of genes associated with glial activation and partially restored OL-specific transcripts to baseline levels. Finally, conditioned medium from activated microglia reduced OL-gene expression in primary OLs without altering their viability. Conclusions These findings show that peripheral respiratory viral infection with IAV is capable of altering OL homeostasis and indicate that microglia activation is likely involved in the process.

Abstract Differential gene expression in bulk transcriptomics data can reflect change of transcript abundance within a cell type and/or change in the proportions of cell types. Expression deconvolution methods can help differentiate these scenarios. BEDwARS is a Bayesian deconvolution method designed to address differences between reference signatures of cell types and corresponding true signatures underlying bulk transcriptomic profiles. BEDwARS is more robust to noisy reference signatures and outperforms leading in-class methods for estimating cell type proportions and signatures. Application of BEDwARS to dihydropyridine dehydrogenase deficiency identified the possible involvement of ciliopathy and impaired translational control in the etiology of the disorder.

Abstract Background Many long non-coding RNAs, known to be involved in transcriptional regulation, are enriched in the nucleus and interact with chromatin. However, their mechanisms of chromatin interaction and the served cellular functions are poorly understood. We sought to characterize the mechanisms of lncRNA nuclear retention by systematically mapping the sequence and chromatin features that distinguish lncRNA-interacting genomic segments. Results We found DNA 5-mer frequencies to be predictive of chromatin interactions for all lncRNAs, suggesting sequence-specificity as a global theme in the interactome. Sequence features representing protein-DNA and protein-RNA binding motifs revealed potential mechanisms for specific lncRNAs. Complementary to these global themes, transcription-related features and DNA-RNA triplex formation potential were noted to be highly predictive for two mutually exclusive sets of lncRNAs. DNA methylation was also noted to be a significant predictor, but only when combined with other epigenomic features. Conclusions Taken together, our statistical findings suggest that a group of lncRNAs interacts with transcriptionally inactive chromatin through triplex formation, whereas another group interacts with transcriptionally active regions and is involved in DNA Damage Response (DDR) through formation of R-loops. Curiously, we observed a strong pattern of enrichment of 5-mers in four potentially interacting entities: lncRNA-bound DNA tiles, lncRNAs, miRNA seed sequences, and repeat elements. This finding points to a broad sequence-based network of interactions that may underlie regulation of fundamental cellular functions. Overall, this study reveals diverse sequence and chromatin features related to lncRNA-chromatin interactions, suggesting potential mechanisms of nuclear retention and regulatory function.

Out of the several hundred copies of rRNA genes arranged in the nucleolar organizing regions (NOR) of the five human acrocentric chromosomes, ~50% remain transcriptionally inactive. NOR-associated sequences and epigenetic modifications contribute to the differential expression of rRNAs. However, the mechanism(s) controlling the dosage of active versus inactive rRNA genes within each NOR in mammals is yet to be determined. We have discovered a family of ncRNAs, SNULs (Single NUcleolus Localized RNA), which form constrained sub-nucleolar territories on individual NORs and influence rRNA expression. Individual members of the SNULs monoallelically associate with specific NOR-containing chromosomes. SNULs share sequence similarity to pre-rRNA and localize in the sub-nucleolar compartment with pre-rRNA. Finally, SNULs control rRNA expression by influencing pre-rRNA sorting to the DFC compartment and pre-rRNA processing. Our study discovered a novel class of ncRNAs influencing rRNA expression by forming constrained nucleolar territories on individual NORs.

ABSTRACT Because of its natural stress tolerance to low pH, Issatchenkia orientalis (a.k.a. Pichia kudriavzevii) is a promising non-model yeast for bio-based production of organic acids. Yet, this organism is relatively unstudied, and specific mechanisms of its tolerance to low pH are poorly understood, limiting commercial use. In this study, we selected 12 I. orientalis strains with varying acid stress tolerance (six tolerant and six susceptible) and profiled their transcriptomes in different pH conditions to study potential mechanisms of pH tolerance in this species. We identified hundreds of genes whose expression response is shared by tolerant strains but not by susceptible strains, or vice versa, as well as genes whose responses are reversed between tolerant and susceptible strains. We mapped regulatory mechanisms of transcriptomic responses via motif analysis as well as differential network reconstruction, identifying several transcription factors, including Stb5, Mac1, and Rtg1/Rtg3, some of which are known for their roles in acid response in Saccharomyces cerevisiae. Functional genomics analysis of short-listed genes and transcription factors suggested significant roles for energy metabolism and translation-related processes, as well as the cell wall integrity pathway and RTG-dependent retrograde signaling pathway. Finally, we conducted additional experiments for two organic acids, 3-hydroxypropionate and citramalate, to eliminate acid-specific effects and found potential roles for glycolysis and trehalose biosynthesis specifically for response to low pH. In summary, our approach of comparative transcriptomics and phenotypic contrasting, along with a multi-pronged bioinformatics analysis, suggests specific mechanisms of tolerance to low pH in I. orientalis that merit further validation through experimental perturbation and engineering. IMPORTANCE Issatchenkia orientalis is a promising industrial chassis to produce biofuels and bioproducts due to its high tolerance to multiple environmental stresses such as low pH, heat, and other chemicals otherwise toxic for the most widely used microbes. Yet, little is known about specific mechanisms of such tolerance in this organism, hindering our ability to engineer this species to produce valuable biochemicals. Here, we report a comprehensive study of the mechanisms of acidic tolerance in this species via transcriptome profiling across variable pH for 12 different strains with different phenotypes. We found multiple regulatory mechanisms involved in tolerance to low pH in different strains of I. orientalis, marking potential targets for future gene editing and perturbation experiments.

Innovation and evolution are paramount where the demand for wideband, data-intensive connectivity is ever-increasing, and the only constant is change. Standards that define the operation of next-generation mobile networks are moving away from the traditional radio frequency (RF) spectrum and into millimeter-wave (mm-wave) bands. The physical layer (PHY) for IEEE 802.11ad Wi-Fi and 802.11ay WLANs dictates operation in the unlicensed 60 GHz band. 5G New Radio (NR) applications utilize selected bands from 26 to 39 GHz. Additionally, newly developing Fourth Industrial Revolution (4IR) applications depend on 5G NR as an enabling technology. Satellite communications and wireless backhaul will occur in E-band between 70–86 GHz. Increasing demands from the market cause designers to push boundaries, and the development of standards guide technological advances. Perhaps the most substantial improvements are observed in integrated circuit technology. This article details the major Si processes, namely Complementary Metal Oxide Semiconductor (CMOS) and SiGe Bipolar CMOS (BiCMOS), and their foray into the wireless transceiver space traditionally dominated by GaAs. CMOS and BiCMOS have become popular in many communities because of their low fabrication cost and excellent digital integration capabilities. RF performance has matured to where Si is a serious competitor for tried-and-tested III-V technologies. Some extreme environments and sophisticated applications still favor GaAs and GaN, however. GaAs, for example, can yield unparalleled output power and excellent noise figure performance, albeit at a higher cost and increased design and manufacturing complexity due to the multi-chip nature of these circuits.

The topic of the Fourth Industrial Revolution (4IR) became significant in South Africa from 2017, through advocacy, amongst others, by the University of Johannesburg and subsequently through the appointment of the Presidential Commission on 4IR. Preceding industrial revolutions each focused on a single technology; 4IR, however, speaks to a confluence of technologies and a synergy of computing, data, and communications technology, with artificial intelligence rapidly redefining the world of work. Conceptual and geopolitical challenges and potential negative societal implications notwithstanding, we argue that the 4IR paradigm shift is critical to South Africa and to realising the Sustainable Development Goals.

BackgroundCerebrospinal fluid rhinorrhoea (CSFR) is a common complication following endonasal skull base surgery, a technique that is fundamental to the treatment of pituitary adenomas and many other skull base tumours. The CRANIAL study explored CSFR incidence and related risk factors, particularly skull base repair techniques, via a multicentre prospective observational study. We sought to use machine learning to leverage this complex multicentre dataset for CSFR prediction and risk factor analysis.MethodsA dataset of 865 cases - 725 transsphenoidal approach (TSA) and 140 expanded endonasal approach (EEA) - with cerebrospinal fluid rhinorrhoea as the primary outcome, was used. Relevant variables were extracted from the data, and prediction variables were divided into two categories, preoperative risk factors; and repair techniques, with 6 and 11 variables respectively. Three types of machine learning models were developed in order to predict CSFR: logistic regression (LR); decision tree (DT); and neural network (NN). Models were validated using 5-fold cross-validation, compared via their area under the curve (AUC) evaluation metric, and key prediction variables were identified using their Shapley additive explanations (SHAP) score.ResultsCSFR rates were 3.9% (28/725) for the transsphenoidal approach and 7.1% (10/140) for the expanded endonasal approach. NNs outperformed LR and DT for CSFR prediction, with a mean AUC of 0.80 (0.70-0.90) for TSA and 0.78 (0.60-0.96) for EEA, when all risk factor and intraoperative repair data were integrated into the model. The presence of intraoperative CSF leak was the most prominent risk factor for CSFR. Elevated BMI and revision surgery were also associated with CSFR for the transsphenoidal approach. CSF diversion and gasket sealing appear to be strong predictors of the absence of CSFR for both approaches.ConclusionNeural networks are effective at predicting CSFR and uncovering key CSFR predictors in patients following endonasal skull base surgery, outperforming traditional statistical methods. These models will be improved further with larger and more granular datasets, improved NN architecture, and external validation. In the future, such predictive models could be used to assist surgical decision-making and support more individualised patient counselling.

Data gathering and information processing have evolved to where it is almost unfathomable how much exists in digital form today. The generation thereof also no longer involves an explicit instruction from human to machine but can happen in real-time without human intervention. Artificial intelligence, machine learning, and cognitive computing are being utilized to mine data from a variety of sources. One such (profitable) source is human beings. Digital algorithms are designed to harness the power of technology to gather information. There has always been a sense of secrecy regarding some information (classified, top secret, confidential, etc.) but the Fourth Industrial Revolution has created the means to gather extremely large amounts of data, unknown to its sources. Anthropological value systems should become a fundamental foundation of digital algorithms. Such an approach could prevent software from exploiting its sources, especially minorities. Value systems together with ethics are guided by people’s culture. In ethically aligned algorithm design, value systems and digital technologies intersect and govern how algorithms are developed, the way data is engaged, and further the discipline of digital humanities.

ObjectiveDespite progress in endonasal skull-base neurosurgery, cerebrospinal fluid (CSF) rhinorrhoea remains common and significant. The CRANIAL study sought to determine 1) the scope of skull-base repair methods used, and 2) corresponding rates of postoperative CSF rhinorrhoea in the endonasal transsphenoidal approach (TSA) and the expanded endonasal approach (EEA) for skull-base tumors.MethodsA prospective observational cohort study of 30 centres performing endonasal skull-base neurosurgery in the UK and Ireland (representing 91% of adult units). Patients were identified for 6 months and followed up for 6 months. Data collection and analysis was guided by our published protocol and pilot studies. Descriptive statistics, univariate and multivariable logistic regression models were used for analysis.ResultsA total of 866 patients were included - 726 TSA (84%) and 140 EEA (16%). There was significant heterogeneity in repair protocols across centres. In TSA cases, nasal packing (519/726, 72%), tissue glues (474/726, 65%) and hemostatic agents (439/726, 61%) were the most common skull base repair techniques. Comparatively, pedicled flaps (90/140, 64%), CSF diversion (38/140, 27%), buttresses (17/140, 12%) and gasket sealing (11/140, 9%) were more commonly used in EEA cases. CSF rhinorrhoea (biochemically confirmed or requiring re-operation) occurred in 3.9% of TSA (28/726) and 7.1% of EEA (10/140) cases. A significant number of patients with CSF rhinorrhoea (15/38, 39%) occurred when no intraoperative CSF leak was reported. On multivariate analysis, there may be marginal benefits with using tissue glues in TSA (OR: 0.2, CI: 0.1-0.7, p

Background: Osteopetrosis is a collection of illnesses with increased bone density on plain radiographs that are clinically and genetically heterogeneous. There is limited literature on the technical difficulties and pitfalls of operative procedures in patients with osteopetrosis. Presentation of case: An 11-year-old male patient presented with pain in the left thigh with the inability to bear weight on the limb following a fall from stairs and was diagnosed as a case of osteopetrosis based on the history, radiological and laboratory findings. He was managed successfully with open reduction and internal fixation with locking compression plate using a machine drill bit. Conclusion: Locking Compression plating is an effective treatment option for shaft femur fractures in patients with osteopetrosis. The use of a machine drill bit can significantly reduce the operative time and postoperative complications by decreasing thermal necrosis.

Abstract Reconstruction of transcriptional regulatory networks (TRNs) is a powerful approach to unravel the gene expression programs involved in healthy and disease states of a cell. However, these networks are usually reconstructed independent of the phenotypic (or clinical) properties of the samples. Therefore, they may confound regulatory mechanisms that are specifically related to a phenotypic property with more general mechanisms underlying the full complement of the analyzed samples. In this study, we develop a method called InPheRNo to identify “phenotype-relevant” TRNs. This method is based on a probabilistic graphical model that models the simultaneous effects of multiple transcription factors (TFs) on their target genes and the statistical relationship between the target genes’ expression and the phenotype. Extensive comparison of InPheRNo with related approaches using primary tumor samples of 18 cancer types from The Cancer Genome Atlas reveals that InPheRNo can accurately reconstruct cancer type-relevant TRNs and identify cancer driver TFs. In addition, survival analysis reveals that the activity level of TFs with many target genes could distinguish patients with poor prognosis from those with better prognosis.

Abstract Background Metastatic progress is the primary cause of death in most cancers, yet the regulatory dynamics driving the cellular changes necessary for metastasis remain poorly understood. Multi-omics approaches hold great promise for addressing this challenge; however, current analysis tools have limited capabilities to systematically integrate transcriptomic, epigenomic, and cistromic information to accurately define the regulatory networks critical for metastasis. Results To address this limitation, we use a purposefully generated cellular model of colon cancer invasiveness to generate multi-omics data, including expression, accessibility, and selected histone modification profiles, for increasing levels of invasiveness. We then adopt a rigorous probabilistic framework for joint inference from the resulting heterogeneous data, along with transcription factor binding profiles. Our approach uses probabilistic graphical models to leverage the functional information provided by specific epigenomic changes, models the influence of multiple transcription factors simultaneously, and automatically learns the activating or repressive roles of cis-regulatory events. Global analysis of these relationships reveals key transcription factors driving invasiveness, as well as their likely target genes. Disrupting the expression of one of the highly ranked transcription factors JunD, an AP-1 complex protein, confirms functional relevance to colon cancer cell migration and invasion. Transcriptomic profiling confirms key regulatory targets of JunD, and a gene signature derived from the model demonstrates strong prognostic potential in TCGA colorectal cancer data. Conclusions Our work sheds new light into the complex molecular processes driving colon cancer metastasis and presents a statistically sound integrative approach to analyze multi-omics profiles of a dynamic biological process.

Summary: Transcription factors (TFs) influence gene expression by binding to DNA, yet experimental data suggests that they also frequently bind regulatory DNA indirectly by interacting with other DNA-bound proteins. Here, we used a data modeling approach to test if such indirect binding by TFs plays a significant role in gene regulation. We first incorporated regulatory function of indirectly bound TFs into a thermodynamics-based model for predicting enhancer-driven expression from its sequence. We then fit the new model to a rich data set comprising hundreds of enhancers and their regulatory activities during mesoderm specification in Drosophila embryogenesis and showed that the newly incorporated mechanism results in significantly better agreement with data. In the process, we derived the first sequence-level model of this extensively characterized regulatory program. We further showed that allowing indirect binding of a TF explains its localization at enhancers more accurately than with direct binding only. Our model also provided a simple explanation of how a TF may switch between activating and repressive roles depending on context.

Abstract Background Cancer cells are known to display varying degrees of metastatic propensity, but the molecular basis underlying such heterogeneity remains unclear. Our aims in this study were to (i) elucidate prognostic subtypes in primary tumors based on an epithelial-to-mesenchymal-to-amoeboid transition (EMAT) continuum that captures the heterogeneity of metastatic propensity and (ii) to more comprehensively define biologically informed subtypes predictive of breast cancer metastasis and survival in lymph node-negative (LNN) patients. Methods We constructed a novel metastasis biology-based gene signature (EMAT) derived exclusively from cancer cells induced to undergo either epithelial-to-mesenchymal transition (EMT) or mesenchymal-to-amoeboid transition (MAT) to gauge their metastatic potential. Genome-wide gene expression data obtained from 913 primary tumors of lymph node-negative breast cancer (LNNBC) patients were analyzed. EMAT gene signature-based prognostic stratification of patients was performed to identify biologically relevant subtypes associated with distinct metastatic propensity. Results Delineated EMAT subtypes display a biologic range from less stem-like to more stem-like cell states and from less invasive to more invasive modes of cancer progression. Consideration of EMAT subtypes in combination with standard clinical parameters significantly improved survival prediction. EMAT subtypes outperformed prognosis accuracy of receptor or PAM50-based BC intrinsic subtypes even after adjusting for treatment variables in 3 independent, LNNBC cohorts including a treatment-naïve patient cohort. Conclusions EMAT classification is a biologically informed method that provides prognostic information beyond that which can be provided by traditional cancer staging or PAM50 molecular subtype status and may improve metastasis risk assessment in early stage, LNNBC patients, who may otherwise be perceived to be at low metastasis risk.

Background and Purpose: Dysfunction of the blood–brain-barrier (BBB) is a recognized pathological consequence of traumatic brain injury (TBI) which may play an important role in chronic TBI pathophysiology. We hypothesized that BBB disruption can be detected with dynamic contrast-enhanced (DCE) MRI not only in association with focal traumatic lesions but also in normal-appearing brain tissue of TBI patients, reflecting microscopic microvascular injury. We further hypothesized that BBB integrity would improve but not completely normalize months after TBI. Materials and Methods: DCE MRI was performed in 40 adult patients a median of 23 days after hospitalized TBI and in 21 healthy controls. DCE data was analyzed using Patlak and linear models, and derived metrics of BBB leakage including the volume transfer constant (Ktrans) and the normalized permeability index (NPI) were compared between groups. BBB metrics were compared with focal lesion distribution as well as with contemporaneous measures of symptomatology and cognitive function in TBI patients. Finally, BBB metrics were examined longitudinally among 18 TBI patients who returned for a second MRI a median of 204 days postinjury. Results: TBI patients exhibited higher mean Ktrans (p = 0.0028) and proportion of suprathreshold NPI voxels (p = 0.001) relative to controls. Tissue-based analysis confirmed greatest TBI-related BBB disruption in association with focal lesions, however elevated Ktrans was also observed in perilesional (p = 0.011) and nonlesional (p = 0.044) regions. BBB disruption showed inverse correlation with quality of life (rho = −0.51, corrected p = 0.016). Among the subset of TBI patients who underwent a second MRI several months after the initial evaluation, metrics of BBB disruption did not differ significantly at the group level, though variable longitudinal changes were observed at the individual subject level. Conclusions: This pilot investigation suggests that TBI-related BBB disruption is detectable in the early post-injury period in association with focal and diffuse brain injury.

Adults of many species will care for young offspring that are not their own, a phenomenon called alloparenting. However, in many cases, nonparental adults must be sensitized by repeated or extended exposures to newborns before they will robustly display parental-like behaviors. To capture neurogenomic events underlying the transition to active parental caring behaviors, we analyzed brain gene expression and chromatin profiles of virgin female mice co-housed with pregnant dams during pregnancy and after birth. After an initial display of antagonistic behaviors and a surge of defense-related gene expression, we observed a dramatic shift in the chromatin landscape specifically in amygdala of the pup-exposed virgin females compared to females co-housed with mother before birth, accompanied by a dampening of anxiety-related gene expression. This epigenetic shift coincided with hypothalamic expression of the oxytocin gene and the emergence of behaviors and gene expression patterns classically associated with maternal care. The results outline a neurogenomic program associated with dramatic behavioral changes and suggest molecular networks relevant to human postpartum mental health.

Existing efforts have been focused on one of the elements in the automation of the design, build, test, and learn (DBTL) cycle for biosystems design. Here, the authors integrate a robotic system with machine learning algorithms to fully automate the DBTL cycle and apply it in optimizing the lycopene biosynthetic pathway.

Abstract Background Identification of functional non-coding variants and their mechanistic interpretation is a major challenge of modern genomics, especially for precision medicine. Transcription factor (TF) binding profiles and epigenomic landscapes in reference samples allow functional annotation of the genome, but do not provide ready answers regarding the effects of non-coding variants on phenotypes. A promising computational approach is to build models that predict TF-DNA binding from sequence, and use such models to score a variant’s impact on TF binding strength. Here, we asked if this mechanistic approach to variant interpretation can be combined with information on genotype-phenotype associations to discover transcription factors regulating phenotypic variation among individuals. Results We developed a statistical approach that integrates phenotype, genotype, gene expression, TF ChIP-seq, and Hi-C chromatin interaction data to answer this question. Using drug sensitivity of lymphoblastoid cell lines as the phenotype of interest, we tested if non-coding variants statistically linked to the phenotype are enriched for strong predicted impact on DNA binding strength of a TF and thus identified TFs regulating individual differences in the phenotype. Our approach relies on a new method for predicting variant impact on TF-DNA binding that uses a combination of biophysical modeling and machine learning. We report statistical and literature-based support for many of the TFs discovered here as regulators of drug response variation. We show that the use of mechanistically driven variant impact predictors can identify TF-drug associations that would otherwise be missed. We examined in depth one reported association—that of the transcription factor ELF1 with the drug doxorubicin—and identified several genes that may mediate this regulatory relationship. Conclusion Our work represents initial steps in utilizing predictions of variant impact on TF binding sites for discovery of regulatory mechanisms underlying phenotypic variation. Future advances on this topic will be greatly beneficial to the reconstruction of phenotype-associated gene regulatory networks.

Abstract Background The NTRK2 genetic locus encodes neurotrophin membrane receptors that play an important role in normal neural tissue plasticity, growth, and survival. One NTRK2-encoded protein is TrkB-FL, which can regulate multiple pathways relevant to cancer. A second NTRK2 gene mRNA isoform encodes TrkB-T1, a receptor that has a different cytoplasmic domain encoded in a mRNA with a unique 3′ terminal exon. Method Tumors from The Cancer Genome Atlas (TCGA) and other studies were classified according to the expression of a single form of NTRK2 mRNA, TrkB-T1, identified by its unique 3′ terminal exon. Analysis of differentially expressed genes in TrkB-T1 high expressers was done to determine if tumors enriched for TrkB-T1 mRNA were a uniform group independent of anatomic site. Results The mRNA for TrkB-T1 is the most abundant NTRK2 gene mRNA in all squamous cell carcinomas (SCCs) in the TCGA database. Comparison of larynx SCC high TrkB-T1 RNA expressers to low expressers (n = 96) revealed gene expression differences consistent with the high TrkB-T1 tumors being more neural-like. The upregulated genes in the TrkB-T1 RNA high expressers also showed enrichment of pathways involved in retinol metabolism, hedgehog signaling, and the Nfe2l2 response, among other pathways. An examination of oral, esophagus, and lung SCCs (n = 284, 97, 501) showed induction of the same pathways among tumors that expressed high levels of TrkB-T1 mRNA. Proteins associated with regulation of the sonic hedgehog pathway, and the Nfe2l2 response, Tp63, and Keap1 and p62/SQSTM1 proteins, showed differential expression in larynx, oral and lung high TrkB1-T1 expresser SCCs. Unexpectantly, the relationship of high level TrkB-T1 expression to patient outcomes was SCC anatomic site specific. High TrkB-T1 mRNA levels in laryngeal SCC correlated with poor survival, but the opposite was true for lung SCC. This may be because pathways enriched in the TrkB high expressers, like those involving oncogenes NFE2L2, PIK3CA, and SOX2, are known to have SCC anatomic site-specific effects on progression. Conclusions High level TrkB-T1 mRNA is a marker of a distinct SCC subtype enriched for at least 3 pathways relevant to tumor progression: Nfe2l2 response, retinol metabolism, and hedgehog signaling.

Osteosarcoma (OS) is the most common form of primary bone cancer in humans. The early detection and subsequent control of metastasis has been challenging in OS. Lipids are important constituents of cells that maintain structural integrity that can be converted into lipid-signaling molecules and are reprogrammed in cancerous states. Here, we investigate the global lipidomic differences in metastatic (143B) and nonmetastatic (HOS) human OS cells as compared with normal fetal osteoblast cells (FOB) using lipidomics. We detect 15 distinct lipid classes in all three cell lines that included over 1,000 lipid species across various classes including phospholipids, sphingolipids and ceramides, glycolipids, and cholesterol. We identify a key class of lipids, diacylglycerols, which are overexpressed in metastatic OS cells as compared with their nonmetastatic or nontumorigenic counterparts. As a proof of concept, we show that blocking diacylglycerol synthesis reduces cellular viability and reduces cell migration in metastatic OS cells. Thus, the differentially regulated lipids identified in this study might aid in biomarker discovery, and the synthesis and metabolism of specific lipids could serve as future targets for therapeutic development.

The increasing popularity of electric vehicles (EVs) has been attributed to their low-carbon and environmentally friendly attributes. Extensive research has been undertaken in view of the depletion of fossil fuels, changes in climatic conditions due to air pollution, and the goal of developing EVs capable of matching or exceeding the performance of today’s internal combustion engines (ICEs). The transition from ICE vehicles to EVs can reduce greenhouse gases significantly over a vehicle’s lifetime. Across the different types of EVs, the widespread usage of batteries is due to their high power density and steady output voltage, making them an excellent energy storage device (ESD). The current downsides of battery-powered electric vehicles include long recharge times, the impact of additional strain on the grid, poor societal acceptance due to high initial costs, and a lack of adequate charging infrastructure. Even more problematic is their short driving range when compared to standard ICE and fuel cell EVs. Battery degradation occurs when the capacity of a battery degrades, resulting in a reduction in travel range. This review article includes a description of battery degradation, degradation mechanisms, and types of degradation. A detailed investigation of the methods used to address and reduce battery degeneration is presented. Finally, some future orientation in terms of EV research is offered as vital guidance for academic and industrial partners.

Introduction Chiari 1 malformation (CM1) is a structural abnormality of the hindbrain characterised by the descent of the cerebellar tonsils through the foramen magnum. The management of patients with CM1 remains contentious since there are currently no UK or international guidelines for clinicians. We therefore propose a collaborative, prospective, multicentre study on the investigation, management and outcome of CM1 in the UK: the UK Chiari 1 Study (UKC1S). Our primary objective is to determine the health-related quality of life (HRQoL) in patients with a new diagnosis of CM1 managed either conservatively or surgically at 12 months of follow-up. We also aim to: (A) determine HRQoL 12 months following surgery; (B) measure complications 12 months following surgery; (C) determine the natural history of patients with CM1 treated conservatively without surgery; (D) determine the radiological correlates of presenting symptoms, signs and outcomes; and (E) determine the scope and variation within UK practice in referral patterns, patient pathways, investigations and surgical decisions.Methods and analysis The UKC1S will be a prospective, multicentre and observational study that will follow the British Neurosurgical Trainee Research Collaborative model of collaborative research. Patients will be recruited after attending their first neurosurgical outpatient clinic appointment. Follow-up data will be collected from all patients at 12 months from baseline regardless of whether they are treated surgically or not. A further 12-month postoperative follow-up timepoint will be added for patients treated with decompressive surgery. The study is expected to last three years.Ethics and dissemination The UKC1S received a favourable ethical opinion from the East Midlands Leicester South Research Ethics Committee (REC reference: 20/EM/0053; IRAS 269739) and the Health Research Authority. The results of the study will be published in peer-reviewed medical journals, presented at scientific conferences, shared with collaborating sites and shared with participant patients if they so wish.

Bioflavonoids are the largest group of plant-derived polyphenolic compounds with diverse biological potential and have also been proven efficacious in the treatment of Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS). The present investigation validates molecular docking, simulation, and MM-PBSA studies of fifteen bioactive bioflavonoids derived from plants as a plausible potential antiviral in the treatment of COVID-19. Molecular docking studies for 15 flavonoids on the three SARS CoV-2 proteins, non-structural protein-15 Endoribonuclease (NSP15), the receptor-binding domain of spike protein (RBD of S protein), and main protease (Mpro/3CLpro) were performed and selected protein-ligand complexes were subjected to Molecular Dynamics simulations. The molecular dynamics trajectories were subjected to free energy calculation by the MM-PBSA method. All flavonoids were further assessed for their effectiveness as adjuvant therapy by network pharmacology analysis on the target proteins. The network pharmacology analysis suggests the involvement of selected bioflavonoids in the modulation of multiple signaling pathways like p53, FoxO, MAPK, Wnt, Rap1, TNF, adipocytokine, and leukocyte transendothelial migration which plays a significant role in immunomodulation, minimizing the oxidative stress and inflammation. Molecular docking and molecular dynamics simulation studies illustrated the potential of glycyrrhizic acid, amentoflavone, and mulberroside in inhibiting key SARS-CoV-2 proteins and these results could be exploited further in designing future ligands from natural sources.

Understanding the regulatory architecture of phenotypic variation is a fundamental goal in biology, but connections between gene regulatory network (GRN) activity and individual differences in behavior are poorly understood. We characterized the molecular basis of behavioral plasticity in queenless honey bee (Apis mellifera) colonies, where individuals engage in both reproductive and non-reproductive behaviors. Using high-throughput behavioral tracking, we discovered these colonies contain a continuum of phenotypes, with some individuals specialized for either egg-laying or foraging and ‘generalists’ that perform both. Brain gene expression and chromatin accessibility profiles were correlated with behavioral variation, with generalists intermediate in behavior and molecular profiles. Models of brain GRNs constructed for individuals revealed that transcription factor (TF) activity was highly predictive of behavior, and behavior-associated regulatory regions had more TF motifs. These results provide new insights into the important role played by brain GRN plasticity in the regulation of behavior, with implications for social evolution.

Summary: Better tools are needed to enable researchers to quickly identify and explore effective and interpretable feature-based explanations for discriminating multi-class genomic datasets, e.g., healthy versus diseased samples. We develop an interactive exploration tool, GENVISAGE, which rapidly discovers the most discriminative feature pairs that separate two classes of genomic objects and then displays the corresponding visualizations. Since quickly finding top feature pairs is computationally challenging, especially for large numbers of objects and features, we propose a suite of optimizations to make GENVISAGE responsive at scale and demonstrate that our optimizations lead to a 400× speedup over competitive baselines for multiple biological datasets. We apply our rapid and interpretable tool to identify literature-supported pairs of genes whose transcriptomic responses significantly discriminate several chemotherapy drug treatments. With its generalizable optimizations and framework, GENVISAGE opens up real-time feature-based explanation generation to data from massive sequencing efforts, as well as many other scientific domains. The Bigger Picture: A fundamental task in the analysis of genomics datasets is identifying features that can explain the difference between two groups of biological samples. As studies and data repositories that enable simultaneous analysis of thousands of samples become widespread, it is imperative that feature identification tools return interpretable and significant results rapidly, allowing researchers to interactively generate and explore hypotheses on these massive datasets. Our tool, GENVISAGE, is built around a framework that identifies pairs of features that strongly separate samples of different classes. An extensive suite of optimization techniques enables us to extract literature-supported feature pairs with accompanying interpretable visualizations from exceptionally large genomic datasets in real time. The GENVISAGE optimizations and webserver instance provide a blueprint for future online tools providing interactive feature exploration in massive datasets from genomics and other domains.

Introduction: Infected non-union of Tibia is one of the most commonly faced problem in both compound as well as closed fractures of Tibia, treated surgically. Few patients also present with gap at the fracture site which may be either due to bone loss during trauma or due to debridement of dead bone during previous surgeries. Treatment of infected non-union is always challenging with unpredictable outcomes. Limb Reconstruction System (LRS) is one of the systems available to treat this complex situation. Aim: To evaluate the role of LRS in treatment of infected nonunion of Tibia in terms of union time, total duration of fixator applied and Visual Analouge Score (VAS). Materials and Methods: Twenty one patients of infected gap, non-union of tibia were included in the study and were treated with debridement, resection of dead bone and application of LRS and segment transport. The results were evaluated in terms of union time, total duration of fixator applied and VAS. Bony and functional assessment was done by Association for the Study and Application of the Methods of Illizarov (ASAMI) criteria. SPSS statistics 24.0 was used for analysis. Mean, median and mode were used to describe continuous variable. Results: Out of 21 patients, 19 were males and two were females. The mean age of patients was 29.43±14.07 years. The mean limb length discrepancy was 23.3 mm (range, 15- 40 mm). The mean duration from injury to LRS application was 7.9 months (range, 6-12 months). Mean duration of follow-up was 29.5 months (range, 16-50 months). Average union time was 44 weeks and average fixator time was 11.2 months. Bony and function results were good and excellent in 90% cases. Conclusion: The use of monolateral rail external fixator LRS is an effective method for the treatment of infected non-union of tibia augmented with a fibular strut graft. This provides good results in terms of bony union, subsidence of infection and functional results.

We present Knowledge Engine for Genomics (KnowEnG), a free-to-use computational system for analysis of genomics data sets, designed to accelerate biomedical discovery. It includes tools for popular bioinformatics tasks such as gene prioritization, sample clustering, gene set analysis, and expression signature analysis. The system specializes in "knowledge-guided" data mining and machine learning algorithms, in which user-provided data are analyzed in light of prior information about genes, aggregated from numerous knowledge bases and encoded in a massive "Knowledge Network." KnowEnG adheres to "FAIR" principles (findable, accessible, interoperable, and reuseable): its tools are easily portable to diverse computing environments, run on the cloud for scalable and cost-effective execution, and are interoperable with other computing platforms. The analysis tools are made available through multiple access modes, including a web portal with specialized visualization modules. We demonstrate the KnowEnG system's potential value in democratization of advanced tools for the modern genomics era through several case studies that use its tools to recreate and expand upon the published analysis of cancer data sets.

Prediction of clinical drug response (CDR) of cancer patients, based on their clinical and molecular profiles obtained prior to administration of the drug, can play a significant role in individualized medicine. Machine learning models have the potential to address this issue but training them requires data from a large number of patients treated with each drug, limiting their feasibility. While large databases of drug response and molecular profiles of preclinical in-vitro cancer cell lines (CCLs) exist for many drugs, it is unclear whether preclinical samples can be used to predict CDR of real patients. We designed a systematic approach to evaluate how well different algorithms, trained on gene expression and drug response of CCLs, can predict CDR of patients. Using data from two large databases, we evaluated various linear and non-linear algorithms, some of which utilized information on gene interactions. Then, we developed a new algorithm called TG-LASSO that explicitly integrates information on samples' tissue of origin with gene expression profiles to improve prediction performance. Our results showed that regularized regression methods provide better prediction performance. However, including the network information or common methods of including information on the tissue of origin did not improve the results. On the other hand, TG-LASSO improved the predictions and distinguished resistant and sensitive patients for 7 out of 13 drugs. Additionally, TG-LASSO identified genes associated with the drug response, including known targets and pathways involved in the drugs' mechanism of action. Moreover, genes identified by TG-LASSO for multiple drugs in a tissue were associated with patient survival. In summary, our analysis suggests that preclinical samples can be used to predict CDR of patients and identify biomarkers of drug sensitivity and survival.

Label-free and real-time visualization of the tumor microenvironment is attractive but challenging. Here the authors present an approach for simultaneous autofluorescence functional imaging and second/third harmonic generation imaging of structural features, using a single excitation source.

There has been a shift towards the acceptance of community engagement and service-learning as a pedagogical tool in the fields of engineering, engineering technology and the built environment. The Engineering Projects in Community Service (EPICS) program in the Institute of Electrical and Electronics Engineers (IEEE) was implemented at the University of Johannesburg (UJ) in the Faculty of Engineering and the Built Environment and this article shares the best practices and lessons learned as the program developed. As EPICS-in-IEEE requires, the partners included UJ students and faculty members, a non-governmental organization (NGO), Engineers without Borders-UJ, and secondary school learners (the pre-university component) from UJ Metropolitan Academy. The EPICS-in-IEEE technical design and development phase was constituted and used as a guideline. The results of this project demonstrated at least six positive implications: university final-year/capstone engineering design projects and exit-level outcomes were achieved; participants worked as a team on engineering-related projects for a local NGO and its community; the project prepared engineering graduates for the professional world; the initiative took the engineering, engineering technology and built environment disciplines to the community through the “social innovation” EPICS-in-IEEE concept; and the UJ value system was instilled while achieving technical/professional outcomes in preparing students for careers in the private, public and non-profit sectors. How to cite this article: HOOSAIN, Mohamed Sameer; SINHA, Saurabh. Integrating ‘Engineering Projects in Community Service’ into engineering curricula to develop graduate attributes. Scholarship of Teaching and Learning in the South, v. 2, n. 1, p. 60-75, Apr. 2018. Available at: http://sotl-south-journal.net/?journal=sotls&page=article&op=view&path%5B%5D=22 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Over the last decade, manufacturing processes have undergone significant change. Most factory activities have been transformed through a set of features built into a smart manufacturing framework. The tools brought to bear by the fourth industrial revolution are critical enablers of such change and progress. This review article describes the series of industrial revolutions and explores traditional manufacturing before presenting various enabling technologies. Insights are offered regarding traditional manufacturing lines where some enabling technologies have been included. The manufacturing supply chain is envisaged as enhancing the enabling technologies of Industry 4.0 through their integration. A systematic literature review is undertaken to evaluate each enabling technology and the manufacturing supply chain and to provide some theoretical synthesis. Similarly, obstacles are listed that must be overcome before a complete shift to smart manufacturing is possible. A brief discussion maps out how the fourth industrial revolution has led to novel manufacturing technologies. Likewise, a review of the fifth industrial revolution is given, and the justification for this development is presented.

Computational models of enhancer function generally assume that transcription factors (TFs) exert their regulatory effects independently, modeling an enhancer as a “bag of sites.” These models fail on endogenous loci that harbor multiple enhancers, and a “two-tier” model appears better suited: in each enhancer TFs work independently, and the total expression is a weighted sum of their expression readouts. Here, we test these two opposing views on how cis-regulatory information is integrated. We fused two Drosophila blastoderm enhancers, measured their readouts, and applied the above two models to these data. The two-tier mechanism better fits these readouts, suggesting that these fused enhancers comprise multiple independent modules, despite having sequence characteristics typical of single enhancers. We show that short-range TF-TF interactions are not sufficient to designate such modules, suggesting unknown underlying mechanisms. Our results underscore that mechanisms of how modules are defined and how their outputs are combined remain to be elucidated.

Abstract Background Identification of genes whose basal mRNA expression predicts the sensitivity of tumor cells to cytotoxic treatments can play an important role in individualized cancer medicine. It enables detailed characterization of the mechanism of action of drugs. Furthermore, screening the expression of these genes in the tumor tissue may suggest the best course of chemotherapy or a combination of drugs to overcome drug resistance. Results We developed a computational method called ProGENI to identify genes most associated with the variation of drug response across different individuals, based on gene expression data. In contrast to existing methods, ProGENI also utilizes prior knowledge of protein–protein and genetic interactions, using random walk techniques. Analysis of two relatively new and large datasets including gene expression data on hundreds of cell lines and their cytotoxic responses to a large compendium of drugs reveals a significant improvement in prediction of drug sensitivity using genes identified by ProGENI compared to other methods. Our siRNA knockdown experiments on ProGENI-identified genes confirmed the role of many new genes in sensitivity to three chemotherapy drugs: cisplatin, docetaxel, and doxorubicin. Based on such experiments and extensive literature survey, we demonstrate that about 73% of our top predicted genes modulate drug response in selected cancer cell lines. In addition, global analysis of genes associated with groups of drugs uncovered pathways of cytotoxic response shared by each group. Conclusions Our results suggest that knowledge-guided prioritization of genes using ProGENI gives new insight into mechanisms of drug resistance and identifies genes that may be targeted to overcome this phenomenon.

Basal gene expression levels have been shown to be predictive of cellular response to cytotoxic treatments. However, such analyses do not fully reveal complex genotype- phenotype relationships, which are partly encoded in highly interconnected molecular networks. Biological pathways provide a complementary way of understanding drug response variation among individuals. In this study, we integrate chemosensitivity data from a large-scale pharmacogenomics study with basal gene expression data from the CCLE project and prior knowledge of molecular networks to identify specific pathways mediating chemical response. We first develop a computational method called PACER, which ranks pathways for enrichment in a given set of genes using a novel network embedding method. It examines a molecular network that encodes known gene-gene as well as gene-pathway relationships, and determines a vector representation of each gene and pathway in the same low-dimensional vector space. The relevance of a pathway to the given gene set is then captured by the similarity between the pathway vector and gene vectors. To apply this approach to chemosensitivity data, we identify genes whose basal expression levels in a panel of cell lines are correlated with cytotoxic response to a compound, and then rank pathways for relevance to these response-correlated genes using PACER. Extensive evaluation of this approach on benchmarks constructed from databases of compound target genes and large collections of drug response signatures demonstrates its advantages in identifying compound-pathway associations compared to existing statistical methods of pathway enrichment analysis. The associations identified by PACER can serve as testable hypotheses on chemosensitivity pathways and help further study the mechanisms of action of specific cytotoxic drugs. More broadly, PACER represents a novel technique of identifying enriched properties of any gene set of interest while also taking into account networks of known gene-gene relationships and interactions.

Studying a gene's regulatory mechanisms is a tedious process that involves identification of candidate regulators by transcription factor (TF) knockout or over-expression experiments, delineation of enhancers by reporter assays, and demonstration of direct TF influence by site mutagenesis, among other approaches. Such experiments are often chosen based on the biologist's intuition, from several testable hypotheses. We pursue the goal of making this process systematic by using ideas from information theory to reason about experiments in gene regulation, in the hope of ultimately enabling rigorous experiment design strategies. For this, we make use of a state-of-the-art mathematical model of gene expression, which provides a way to formalize our current knowledge of cis- as well as trans- regulatory mechanisms of a gene. Ambiguities in such knowledge can be expressed as uncertainties in the model, which we capture formally by building an ensemble of plausible models that fit the existing data and defining a probability distribution over the ensemble. We then characterize the impact of a new experiment on our understanding of the gene's regulation based on how the ensemble of plausible models and its probability distribution changes when challenged with results from that experiment. This allows us to assess the 'value' of the experiment retroactively as the reduction in entropy of the distribution (information gain) resulting from the experiment's results. We fully formalize this novel approach to reasoning about gene regulation experiments and use it to evaluate a variety of perturbation experiments on two developmental genes of D. melanogaster. We also provide objective and 'biologist-friendly' descriptions of the information gained from each such experiment. The rigorously defined information theoretic approaches presented here can be used in the future to formulate systematic strategies for experiment design pertaining to studies of gene regulatory mechanisms.

Sequence variation within enhancers plays a major role in both evolution and disease, yet its functional impact on transcription factor (TF) occupancy and enhancer activity remains poorly understood. Here, we assayed the binding of five essential TFs over multiple stages of embryogenesis in two distant Drosophila species (with 1.4 substitutions per neutral site), identifying thousands of orthologous enhancers with conserved or diverged combinatorial occupancy. We used these binding signatures to dissect two properties of developmental enhancers: (1) potential TF cooperativity, using signatures of co-associations and co-divergence in TF occupancy. This revealed conserved combinatorial binding despite sequence divergence, suggesting protein-protein interactions sustain conserved collective occupancy. (2) Enhancer in-vivo activity, revealing orthologous enhancers with conserved activity despite divergence in TF occupancy. Taken together, we identify enhancers with diverged motifs yet conserved occupancy and others with diverged occupancy yet conserved activity, emphasising the need to functionally measure the effect of divergence on enhancer activity.

This paper reviews the state of emerging transistor technologies capable of terahertz amplification, as well as the state of transistor modeling as required in terahertz electronic circuit research. Commercial terahertz radar sensors of today are being built using bulky and expensive technologies such as Schottky diode detectors and lasers, as well as using some emerging detection methods. Meanwhile, a considerable amount of research effort has recently been invested in process development and modeling of transistor technologies capable of amplifying in the terahertz band. Indium phosphide (InP) transistors have been able to reach maximum oscillation frequency (fmax) values of over 1 THz for around a decade already, while silicon-germanium bipolar complementary metal-oxide semiconductor (BiCMOS) compatible heterojunction bipolar transistors have only recently crossed the fmax = 0.7 THz mark. While it seems that the InP technology could be the ultimate terahertz technology, according to the fmax and related metrics, the BiCMOS technology has the added advantage of lower cost and supporting a wider set of integrated component types. BiCMOS can thus be seen as an enabling factor for re-engineering of complete terahertz radar systems, for the first time fabricated as miniaturized monolithic integrated circuits. Rapid commercial deployment of monolithic terahertz radar chips, furthermore, depends on the accuracy of transistor modeling at these frequencies. Considerations such as fabrication and modeling of passives and antennas, as well as packaging of complete systems, are closely related to the two main contributions of this paper and are also reviewed here. Finally, this paper probes active terahertz circuits that have already been reported and that have the potential to be deployed in a re-engineered terahertz radar sensor system and attempts to predict future directions in re-engineering of monolithic radar sensors.

Background Glioblastoma is a highly infiltrative, currently incurable brain cancer. To date, translation of novel therapies for glioblastoma from the laboratory into clinical trials has relied heavily on in vitro cell culture and murine (subcutaneous and orthotopic) xenograft models using cells derived from the main bulk of patient tumours. However, it is the residual cells left-behind after surgery that are responsible for disease progression and death in the clinic. A lack of substantial improvements in patient survival for decades suggests commonly used murine xenograft models, a key step before clinical trials, do not reflect the biology of residual disease in patients. Methods To address this, we have developed the ‘Sheffield Protocol’ to generate ex vivo models that reflect both resected, and post-surgical residual disease from the same patient. The protocol leverages parallel derivation of inherently treatment-resistant glioblastoma stem cells (GSCs) from ‘core’ and distant ‘edge’ regions through careful macrodissection of a large en bloc specimen, such as from a partial lobectomy for tumour, followed by tissue dissociation and propagation in serum-free media. Opportunistic en bloc specimen use can liberate the most distant infiltrative cells feasibly accessible from living patients. Results We provide an example illustrating that resected and residual disease models represent spatially divergent tumour subpopulations harbouring distinct transcriptomic and cancer stem cell marker expression profiles. We also introduce the ‘Sheffield Living Biobank’ of glioma models (SLB) that incorporates over 150 GSC lines from 60+ patients, including 44+ resected and residual models, which are available for academic use via MTA. Conclusions These models provide a novel tool to reduce animal xenograft usage by improving candidate drug triage in early preclinical studies and directly replacing animal studies for some therapies that are post-Phase 1+ clinical trial for other cancers/conditions to, ultimately, deliver more effective treatments for post-surgical residual disease in glioblastoma.

In recent times, Wireless Sensor Networks (WSNs) are broadly applied in the Industrial Internet of Things (IIoT) in order to enhance the productivity and efficiency of existing and prospective manufacturing industries. In particular, an area of interest that concerns the use of WSNs in IIoT is the concept of sensor network virtualization and overlay networks. Both network virtualization and overlay networks are considered contemporary because they provide the capacity to create services and applications at the edge of existing virtual networks without changing the underlying infrastructure. This capability makes both network virtualization and overlay network services highly beneficial, particularly for the dynamic needs of IIoT based applications such as in smart industry applications, smart city, and smart home applications. Consequently, the study of both WSN virtualization and overlay networks has become highly patronized in the literature, leading to the growth and maturity of the research area. In line with this growth, this paper provides a review of the development made thus far concerning virtualized sensor networks, with emphasis on the application of overlay networks in IIoT. Principally, the process of virtualization in WSN is discussed along with its importance in IIoT applications. Different challenges in WSN are also presented along with possible solutions given by the use of virtualized WSNs. Further details are also presented concerning the use of overlay networks as the next step to supporting virtualization in shared sensor networks. Our discussion closes with an exposition of the existing challenges in the use of virtualized WSN for IIoT applications. In general, because overlay networks will be contributory to the future development and advancement of smart industrial and smart city applications, this review may be considered by researchers as a reference point for those particularly interested in the study of this growing field.

Background: Periodontitis can be defined as a local inflammatory process which mediates destruction of periodontal tissues & is triggered by bacterial insult. In periodontal infections, the levels of C reactive proteins are elevated as compared to the levels in a periodontally healthy individual. The study was done to determine the relative levels of serum CRP in aggressive, chronic and periodontally healthy subjects and to evaluate the effect of surgical periodontal therapy on serum C-reactive protein levels. Materials and Methods: Serum samples were collected from 150 participants (50 healthy control patients (non-periodontitis), 50 patients with chronic periodontitis and aggressive periodontitis. Serum C- reactive protein levels were assessed by means of immunoturbidimetric assay at baseline for subjects in all the 3 groups and 3 months after completion of surgical therapy. Results: The mean baseline C-reactive protein (CRP) concentrations in the Groups I, II and III were 1.65±0.57 mg/L, 3.03±2.14 mg/L and 3.09±2.27 mg/L respectively. After treatment, the mean C-reactive protein (CRP) levels in Groups II and III reduced from 3.03±1.67 mg/L to 1.46±1.67 mg/L and from 3.09±1.21 to 1.43±1.21 mg/L respectively. Similar results were found for probing depth and all indexes in Group II and III after treatment. Also, the mean attachment loss in Groups II and III reduced, so the results were highly significant. Conclusion: Successful periodontal treatment results in significant decrease in serum C-reactive protein (CRP) levels in otherwise healthy subjects.