Sequential addition of neuronal stem cell temporal cohorts generates a feed-forward circuit in the Drosophila larval nerve cord

How circuits self-assemble starting from neuronal stem cells is a fundamental question in developmental neurobiology. Here, we addressed how neurons from different stem cell lineages wire with each other to form a specific circuit motif. In Drosophila larvae, we combined developmental genetics (Twin spot MARCM, Multi-color Flip Out, permanent labeling) with circuit analysis (calcium imaging, connectomics, network science). For many lineages, neuronal progeny are organized into subunits called temporal cohorts. Temporal cohorts are subsets of neurons born within a tight time window that have shared circuit level function. We find sharp transitions in patterns of input connectivity at temporal cohort boundaries. In addition, we identify a feed-forward circuit that encodes the onset of vibration stimuli. This feed-forward circuit is assembled by preferential connectivity between temporal cohorts from different lineages. Connectivity does not follow the often-cited early-to-early, late-to-late model. Instead, the circuit is formed by sequential addition of temporal cohorts from different lineages, with circuit output neurons born before circuit input neurons. Further, we generate new tools for the fly community. Our data raise the possibility that sequential addition of neurons (with outputs oldest and inputs youngest) could be one fundamental strategy for assembling feed-forward circuits

Towards the Better Ranking Consistency: A Multi-task Learning Framework for Early Stage Ads Ranking

Dividing ads ranking system into retrieval, early, and final stages is a common practice in large scale ads recommendation to balance the efficiency and accuracy. The early stage ranking often uses efficient models to generate candidates out of a set of retrieved ads. The candidates are then fed into a more computationally intensive but accurate final stage ranking system to produce the final ads recommendation. As the early and final stage ranking use different features and model architectures because of system constraints, a serious ranking consistency issue arises where the early stage has a low ads recall, i.e., top ads in the final stage are ranked low in the early stage. In order to pass better ads from the early to the final stage ranking, we propose a multi-task learning framework for early stage ranking to capture multiple final stage ranking components (i.e. ads clicks and ads quality events) and their task relations. With our multi-task learning framework, we can not only achieve serving cost saving from the model consolidation, but also improve the ads recall and ranking consistency. In the online A/B testing, our framework achieves significantly higher click-through rate (CTR), conversion rate (CVR), total value and better ads-quality (e.g. reduced ads cross-out rate) in a large scale industrial ads ranking system.Comment: Accepted by AdKDD 2

Ex vivo explant cultures of non–small cell lung carcinoma enable evaluation of primary tumor responses to anticancer therapy

To improve treatment outcomes in non–small cell lung cancer (NSCLC), preclinical models that can better predict individual patient response to novel therapies are urgently needed. Using freshly resected tumor tissue, we describe an optimized ex vivo explant culture model that enables concurrent evaluation of NSCLC response to therapy while maintaining the tumor microenvironment. We found that approximately 70% of primary NSCLC specimens were amenable to explant culture with tissue integrity intact for up to 72 hours. Variations in cisplatin sensitivity were noted with approximately 50% of cases responding ex vivo. Notably, explant responses to cisplatin correlated significantly with patient survival (P = 0.006) irrespective of tumor stage. In explant tissue, cisplatin-resistant tumors excluded platinum ions from tumor areas in contrast to cisplatin-sensitive tumors. Intact TP53 did not predict cisplatin sensitivity, but a positive correlation was observed between cisplatin sensitivity and TP53 mutation status (P = 0.003). Treatment of NSCLC explants with the targeted agent TRAIL revealed differential sensitivity with the majority of tumors resistant to single-agent or cisplatin combination therapy. Overall, our results validated a rapid, reproducible, and low-cost platform for assessing drug responses in patient tumors ex vivo, thereby enabling preclinical testing of novel drugs and helping stratify patients using biomarker evaluation

A multi-targeted approach to suppress tumor-promoting inflammation

Cancers harbor significant genetic heterogeneity and patterns of relapse following many therapies are due to evolved resistance to treatment. While efforts have been made to combine targeted therapies, significant levels of toxicity have stymied efforts to effectively treat cancer with multi-drug combinations using currently approved therapeutics. We discuss the relationship between tumor-promoting inflammation and cancer as part of a larger effort to develop a broad-spectrum therapeutic approach aimed at a wide range of targets to address this heterogeneity. Specifically, macrophage migration inhibitory factor, cyclooxygenase-2, transcription factor nuclear factor-κB, tumor necrosis factor alpha, inducible nitric oxide synthase, protein kinase B, and CXC chemokines are reviewed as important antiinflammatory targets while curcumin, resveratrol, epigallocatechin gallate, genistein, lycopene, and anthocyanins are reviewed as low-cost, low toxicity means by which these targets might all be reached simultaneously. Future translational work will need to assess the resulting synergies of rationally designed antiinflammatory mixtures (employing low-toxicity constituents), and then combine this with similar approaches targeting the most important pathways across the range of cancer hallmark phenotypes

Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models

Deep learning recommendation models (DLRMs) are used across many business-critical services at Facebook and are the single largest AI application in terms of infrastructure demand in its data-centers. In this paper we discuss the SW/HW co-designed solution for high-performance distributed training of large-scale DLRMs. We introduce a high-performance scalable software stack based on PyTorch and pair it with the new evolution of Zion platform, namely ZionEX. We demonstrate the capability to train very large DLRMs with up to 12 Trillion parameters and show that we can attain 40X speedup in terms of time to solution over previous systems. We achieve this by (i) designing the ZionEX platform with dedicated scale-out network, provisioned with high bandwidth, optimal topology and efficient transport (ii) implementing an optimized PyTorch-based training stack supporting both model and data parallelism (iii) developing sharding algorithms capable of hierarchical partitioning of the embedding tables along row, column dimensions and load balancing them across multiple workers; (iv) adding high-performance core operators while retaining flexibility to support optimizers with fully deterministic updates (v) leveraging reduced precision communications, multi-level memory hierarchy (HBM+DDR+SSD) and pipelining. Furthermore, we develop and briefly comment on distributed data ingestion and other supporting services that are required for the robust and efficient end-to-end training in production environments

Bayesian Inference of Phylogenetic Networks

The multispecies coalescent (MSC) is a statistical framework that models how gene genealogies grow within the branches of a species tree. The field of computational phylogenetics has witnessed an explosion in the development of methods for species tree inference under the MSC, owing mainly to the accumulating evidence of incomplete lineage sorting in phylogenomic analyses. However, the evolutionary history of a set of genomes, or species, could be reticulate due to the occurrence of evolutionary processes such as hybridization or horizontal gene transfer. We devised a novel method for Bayesian inference of genome and species phylogenies under the multispecies network coalescent (MSNC). This framework models gene evolution within the branches of a phylogenetic network, thus incorporating reticulate evolutionary processes, such as hybridization, in addition to incomplete lineage sorting. As phylogenetic networks with different numbers of reticulation events correspond to points of different dimensions in the space of models, we devised a reversible-jump Markov chain Monte Carlo (RJMCMC) technique for sampling the posterior distribution of phylogenetic networks under the MSNC. Given the reticulate evolutionary histories for the whole genome, we devised a method to quantify introgression which would elucidate how each gene evolves. We implemented the methods in the publicly available, open-source software package PhyloNet and studied their performance on simulated and biological data. The work extends the reach of Bayesian inference to phylogenetic networks and enables new evolutionary analyses that account for reticulation

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Bachelor'sBACHELOR OF SCIENCE (REAL ESTATE

Long-term effectiveness and safety of once-daily, single-entity, extended-release hydrocodone in patients of ≥75 years of age with moderate to severe nonmalignant and nonneuropathic pain

AbstractIn elderly (≥75 years) individuals, age-associated physiologic changes and a higher prevalence of comorbidities, polypharmacy, and increased susceptibility to medication-induced side effects complicate pain management. Hysingla® ER (HYD) is a once-daily, single-entity, extended-release hydrocodone formulation approved for the treatment of chronic pain that is insufficiently controlled by alternative treatments. In this post-hoc analysis of a previously reported study, the effectiveness and safety of HYD for the treatment of moderate-to-severe chronic pain among the elderly (≥75 years) for a 52-week duration was investigated. HYD dose administered during the maintenance period-remained relatively stable and provided clinically meaningful decreases in mean “pain over the last 24 h” and pain interference scores. Patients achieved pain control without additional non-study opioid use at the end of the study. Adverse events were typical of opioids. In summary, HYD provided clinically meaningful reduction of pain scores in elderly patients that were maintained over a 52-week period