Abstract: BACKGROUND Patients at high risk for bleeding who undergo percutaneous coronary intervention (PCI) often receive bare-metal stents followed by 1 month of dual antiplatelet therapy. We studied a polymer-free and carrier-free drug-coated stent that transfers umirolimus (also known as biolimus A9), a highly lipophilic sirolimus analogue, into the vessel wall over a period of 1 month. METHODS In a randomized, double-blind trial, we compared the drug-coated stent with a very similar bare-metal stent in patients with a high risk of bleeding who underwent PCI. All patients received 1 month of dual antiplatelet therapy. The primary safety end point, tested for both noninferiority and superiority, was a composite of cardiac death, myocardial infarction, or stent thrombosis. The primary efficacy end point was clinically driven target-lesion revascularization. RESULTS We enrolled 2466 patients. At 390 days, the primary safety end point had occurred in 112 patients (9.4%) in the drug-coated–stent group and in 154 patients (12.9%) in the bare-metal–stent group (risk difference, −3.6 percentage points; 95% confi dence interval [CI], −6.1 to −1.0; hazard ratio, 0.71; 95% CI, 0.56 to 0.91; P<0.001 for noninferiority and P = 0.005 for superiority). During the same time period, clinically driven target-lesion revascularization was needed in 59 patients (5.1%) in the drug-coated–stent group and in 113 patients (9.8%) in the bare-metal–stent group (risk difference, −4.8 percentage points; 95% CI, −6.9 to −2.6; hazard ratio, 0.50; 95% CI, 0.37 to 0.69; P<0.001). CONCLUSIONS Among patients at high risk for bleeding who underwent PCI, a polymer-free umirolimus-coated stent was superior to a bare-metal stent with respect to the primary safety and efficacy end points when used with a 1-month course of dual antiplatelet therapy. (Funded by Biosensors Europe; LEADERS FREE ClinicalTrials .gov number, NCT01623180.)

Abstract: Antibiotic resistance is projected as one of the greatest threats to human health in the future and hence alternatives are being explored to combat resistance. Antimicrobial peptides (AMPs) have shown great promise, because use of AMPs leads bacteria to develop no or low resistance. In this review, we discuss the diversity, history and the various mechanisms of action of AMPs. Although many AMPs have reached clinical trials, to date not many have been approved by the US Food and Drug Administration (FDA) due to issues with toxicity, protease cleavage and short half-life. Some of the recent strategies developed to improve the activity and biocompatibility of AMPs, such as chemical modifications and the use of delivery systems, are also reviewed in this article.

Abstract: Digital supply chain integration is becoming increasingly dynamic. Access to customer demand needs to be shared effectively, and product and service deliveries must be tracked to provide visibility in the supply chain. Business process integration is based on standards and reference architectures, which should offer end-to-end integration of product data. Companies operating in supply chains establish process and data integration through the specialized intermediate companies, whose role is to establish interoperability by mapping and integrating companyspecific data for various organizations and systems. This has typically caused high integration costs, and diffusion is slow. This paper investigates the requirements and functionalities of supply chain integration. Cloud integration can be expected to offer a cost-effective business model for interoperable digital supply chains. We explain how supply chain integration through the blockchain technology can achieve disruptive transformation in digital supply chains and networks.

Abstract: We compare two alternative approaches for evaluating the potential of a work group or team: one that focuses on team members' demographic characteristics and one that focuses on the members' social networks. Given that people's network contacts often share their demographic attributes (i.e., the network is homophilous), the two approaches seem equivalent and the first seems preferable because it is easier to implement. In this paper, we demonstrate several important limits to this rationale. First, we argue and show, in an analysis of 1,518 project teams in a contract research and development firm, that even when internal organizational networks are significantly homophilous with respect to demographic variables, the very logic of the causal structure that underlies theories of demographic diversity carries ambiguous performance implications. This ambiguity is due to the fact that demographic diversity has opposing effects on two social network variables—internal density and external range—each of which h...

Abstract: Endogenous peroxide levels in pear fruit (Pyrus communis) were measured using a titanium assay method, and were found to increase during senescence in both Bartlett and Bosc varieties. Application of glycolic acid or xanthine, serving as substrates for the formation of H202, increased the peroxide content of the tissue and accelerated the onset of ripening, as measured by increased softening and ethylene evolution. Application of ethylene also induced increased peroxide levels. Ripening processes were similarly promoted when peroxides were conserved by inhibiting the activity of catalase with hydroxylamine or potassium cyanide. By comparison, the inhibition of glycolate oxidase with alphahydroxy-2-pyridinemethanesulfonic acid decreased the peroxide content of the tissue and delayed the onset of ripening. These results indicate that the onset of ripening correlates with the peroxide content of fruit tissues as occurring under normal conditions or as influenced by the treatments. Hydrogen peroxide may be involved in oxidative processes required in the initiation and the promotion of ripening.

Abstract: The COVID-19 pandemic has shown a markedly low proportion of cases among children. Age disparities in observed cases could be explained by assortative mixing patterns and reactive school closures which decrease mixing between children, or by children exhibiting lower susceptibility to infection, or by children having a lower propensity to show clinical symptoms. We formally test these hypotheses by fitting an age-structured mathematical model to epidemic data from six countries, finding strong age dependence in the probability of developing clinical symptoms, rising from around 20% in under 10s to over 70% in older adults. We find that interventions aimed at halting transmission in children may have minimal effects on preventing cases depending on the relative transmissibility of subclinical infections. Our estimated age-specific clinical fraction has implications for the expected global burden of clinical cases because of demographic differences across settings. In younger populations, the expected clinical attack rate would be lower, although it is likely that comorbidities in low-income countries will affect disease severity. Without effective control measures, regions with older populations may see disproportionally more clinical cases, particularly in the later stages of the pandemic.

Abstract: Over half of the European landscape is under agricultural management and has been for millennia. Many species and ecosystems of conservation concern in Europe depend on agricultural management and are showing ongoing declines. Agri-environment schemes (AES) are designed partly to address this. They are a major source of nature conservation funding within the European Union (EU) and the highest conservation expenditure in Europe. We reviewed the structure of current AES across Europe. Since a 2003 review questioned the overall effectiveness of AES for biodiversity, there has been a plethora of case studies and meta-analyses examining their effectiveness. Most syntheses demonstrate general increases in farmland biodiversity in response to AES, with the size of the effect depending on the structure and management of the surrounding landscape. This is important in the light of successive EU enlargement and ongoing reforms of AES. We examined the change in effect size over time by merging the data sets of 3 recent meta-analyses and found that schemes implemented after revision of the EU's agri-environmental programs in 2007 were not more effective than schemes implemented before revision. Furthermore, schemes aimed at areas out of production (such as field margins and hedgerows) are more effective at enhancing species richness than those aimed at productive areas (such as arable crops or grasslands). Outstanding research questions include whether AES enhance ecosystem services, whether they are more effective in agriculturally marginal areas than in intensively farmed areas, whether they are more or less cost-effective for farmland biodiversity than protected areas, and how much their effectiveness is influenced by farmer training and advice? The general lesson from the European experience is that AES can be effective for conserving wildlife on farmland, but they are expensive and need to be carefully designed and targeted.

Abstract: The main group of enzymes responsible for the collagen and other protein degradation in extracellular matrix (ECM) are matrix metalloproteinases (MMPs). Collagen is the main structural component of connective tissue and its degradation is a very important process in the development, morphogenesis, tissue remodeling, and repair. Typical structure of MMPs consists of several distinct domains. MMP family can be divided into six groups: collagenases, gelatinases, stromelysins, matrilysins, membrane-type MMPs, and other non-classified MMPs. MMPs and their inhibitors have multiple biological functions in all stages of cancer development: from initiation to outgrowth of clinically relevant metastases and likewise in apoptosis and angiogenesis. MMPs and their inhibitors are extensively examined as potential anticancer drugs. MMP inhibitors can be divided into two main groups: synthetic and natural inhibitors. Selected synthetic inhibitors are in clinical trials on humans, e.g. synthetic peptides, non-peptidic molecules, chemically modified tetracyclines, and bisphosphonates. Natural MMP inhibitors are mainly isoflavonoids and shark cartilage.

Abstract: Deep learning algorithms achieve high classification accuracy at the expense of significant computation cost To address this cost, a number of quantization schemes have been proposed - but most of these techniques focused on quantizing weights, which are relatively smaller in size compared to activations This paper proposes a novel quantization scheme for activations during training - that enables neural networks to work well with ultra low precision weights and activations without any significant accuracy degradation This technique, PArameterized Clipping acTivation (PACT), uses an activation clipping parameter $\alpha$ that is optimized during training to find the right quantization scale PACT allows quantizing activations to arbitrary bit precisions, while achieving much better accuracy relative to published state-of-the-art quantization schemes We show, for the first time, that both weights and activations can be quantized to 4-bits of precision while still achieving accuracy comparable to full precision networks across a range of popular models and datasets We also show that exploiting these reduced-precision computational units in hardware can enable a super-linear improvement in inferencing performance due to a significant reduction in the area of accelerator compute engines coupled with the ability to retain the quantized model and activation data in on-chip memories

Abstract: Importance Clinical outcomes for glioblastoma remain poor. Treatment with immune checkpoint blockade has shown benefits in many cancer types. To our knowledge, data from a randomized phase 3 clinical trial evaluating a programmed death-1 (PD-1) inhibitor therapy for glioblastoma have not been reported. Objective To determine whether single-agent PD-1 blockade with nivolumab improves survival in patients with recurrent glioblastoma compared with bevacizumab. Design, Setting, and Participants In this open-label, randomized, phase 3 clinical trial, 439 patients with glioblastoma at first recurrence following standard radiation and temozolomide therapy were enrolled, and 369 were randomized. Patients were enrolled between September 2014 and May 2015. The median follow-up was 9.5 months at data cutoff of January 20, 2017. The study included 57 multicenter, multinational clinical sites. Interventions Patients were randomized 1:1 to nivolumab 3 mg/kg or bevacizumab 10 mg/kg every 2 weeks until confirmed disease progression, unacceptable toxic effects, or death. Main Outcomes and Measures The primary end point was overall survival (OS). Results A total of 369 patients were randomized to nivolumab (n = 184) or bevacizumab (n = 185). TheMGMTpromoter was methylated in 23.4% (43/184; nivolumab) and 22.7% (42/185; bevacizumab), unmethylated in 32.1% (59/184; nivolumab) and 36.2% (67/185; bevacizumab), and not reported in remaining patients. At median follow-up of 9.5 months, median OS (mOS) was comparable between groups: nivolumab, 9.8 months (95% CI, 8.2-11.8); bevacizumab, 10.0 months (95% CI, 9.0-11.8); HR, 1.04 (95% CI, 0.83-1.30);P = .76. The 12-month OS was 42% in both groups. The objective response rate was higher with bevacizumab (23.1%; 95% CI, 16.7%-30.5%) vs nivolumab (7.8%; 95% CI, 4.1%-13.3%). Grade 3/4 treatment-related adverse events (TRAEs) were similar between groups (nivolumab, 33/182 [18.1%]; bevacizumab, 25/165 [15.2%]), with no unexpected neurological TRAEs or deaths due to TRAEs. Conclusions and Relevance Although the primary end point was not met in this randomized clinical trial, mOS was comparable between nivolumab and bevacizumab in the overall patient population with recurrent glioblastoma. The safety profile of nivolumab in patients with glioblastoma was consistent with that in other tumor types. Trial Registration ClinicalTrials.gov Identifier:NCT02017717

Abstract: Neural network models have been demonstrated to be capable of achieving remarkable performance in sentence and document modeling. Convolutional neural network (CNN) and recurrent neural network (RNN) are two mainstream architectures for such modeling tasks, which adopt totally different ways of understanding natural languages. In this work, we combine the strengths of both architectures and propose a novel and unified model called C-LSTM for sentence representation and text classification. C-LSTM utilizes CNN to extract a sequence of higher-level phrase representations, and are fed into a long short-term memory recurrent neural network (LSTM) to obtain the sentence representation. C-LSTM is able to capture both local features of phrases as well as global and temporal sentence semantics. We evaluate the proposed architecture on sentiment classification and question classification tasks. The experimental results show that the C-LSTM outperforms both CNN and LSTM and can achieve excellent performance on these tasks.

Abstract: Zinc oxide nanoparticles (ZnO NPs) are used in an increasing number of industrial products such as rubber, paint, coating, and cosmetics In the past two decades, ZnO NPs have become one of the most popular metal oxide nanoparticles in biological applications due to their excellent biocompatibility, economic, and low toxicity ZnO NPs have emerged a promising potential in biomedicine, especially in the fields of anticancer and antibacterial fields, which are involved with their potent ability to trigger excess reactive oxygen species (ROS) production, release zinc ions, and induce cell apoptosis In addition, zinc is well known to keep the structural integrity of insulin So, ZnO NPs also have been effectively developed for antidiabetic treatment Moreover, ZnO NPs show excellent luminescent properties and have turned them into one of the main candidates for bioimaging Here, we summarize the synthesis and recent advances of ZnO NPs in the biomedical fields, which will be helpful for facilitating their future research progress and focusing on biomedical fields

Abstract: To truly understand the visual world our models should be able not only to recognize images but also generate them. To this end, there has been exciting recent progress on generating images from natural language descriptions. These methods give stunning results on limited domains such as descriptions of birds or flowers, but struggle to faithfully reproduce complex sentences with many objects and relationships. To overcome this limitation we propose a method for generating images from scene graphs, enabling explicitly reasoning about objects and their relationships. Our model uses graph convolution to process input graphs, computes a scene layout by predicting bounding boxes and segmentation masks for objects, and converts the layout to an image with a cascaded refinement network. The network is trained adversarially against a pair of discriminators to ensure realistic outputs. We validate our approach on Visual Genome and COCO-Stuff, where qualitative results, ablations, and user studies demonstrate our method's ability to generate complex images with multiple objects.

Abstract: Background The body of knowledge regarding rhinosinusitis(RS) continues to expand, with rapid growth in number of publications, yet substantial variability in the quality of those presentations. In an effort to both consolidate and critically appraise this information, rhinologic experts from around the world have produced the International Consensus Statement on Allergy and Rhinology: Rhinosinusitis (ICAR:RS). Methods Evidence-based reviews with recommendations(EBRRs) were developed for scores of topics, using previously reported methodology. Where existing evidence was insufficient for an EBRR, an evidence-based review (EBR)was produced. The sections were then synthesized and the entire manuscript was then reviewed by all authors for consensus. Results The resulting ICAR:RS document addresses multiple topics in RS, including acute RS (ARS), chronic RS (CRS)with and without nasal polyps (CRSwNP and CRSsNP), recurrent acute RS (RARS), acute exacerbation of CRS (AECRS), and pediatric RS. Conclusion As a critical review of the RS literature, ICAR:RS provides a thorough review of pathophysiology and evidence-based recommendations for medical and surgical treatment. It also demonstrates the significant gaps in our understanding of the pathophysiology and optimal management of RS. Too often the foundation upon which these recommendations are based is comprised of lower level evidence. It is our hope that this summary of the evidence in RS will point out where additional research efforts may be directed.

Abstract: In this paper, drawing intuition from the Turing test, we propose using adversarial training for open-domain dialogue generation: the system is trained to produce sequences that are indistinguishable from human-generated dialogue utterances. We cast the task as a reinforcement learning (RL) problem where we jointly train two systems, a generative model to produce response sequences, and a discriminator---analagous to the human evaluator in the Turing test--- to distinguish between the human-generated dialogues and the machine-generated ones. The outputs from the discriminator are then used as rewards for the generative model, pushing the system to generate dialogues that mostly resemble human dialogues. In addition to adversarial training we describe a model for adversarial {\em evaluation} that uses success in fooling an adversary as a dialogue evaluation metric, while avoiding a number of potential pitfalls. Experimental results on several metrics, including adversarial evaluation, demonstrate that the adversarially-trained system generates higher-quality responses than previous baselines.

Abstract: Metasurfaces provide opportunities for wavefront control, flat optics, and subwavelength light focusing. We developed an imaging-based nanophotonic method for detecting mid-infrared molecular fingerprints and implemented it for the chemical identification and compositional analysis of surface-bound analytes. Our technique features a two-dimensional pixelated dielectric metasurface with a range of ultrasharp resonances, each tuned to a discrete frequency; this enables molecular absorption signatures to be read out at multiple spectral points, and the resulting information is then translated into a barcode-like spatial absorption map for imaging. The signatures of biological, polymer, and pesticide molecules can be detected with high sensitivity, covering applications such as biosensing and environmental monitoring. Our chemically specific technique can resolve absorption fingerprints without the need for spectrometry, frequency scanning, or moving mechanical parts, thereby paving the way toward sensitive and versatile miniaturized mid-infrared spectroscopy devices.

Abstract: Organic luminogens with persistent room temperature phosphorescence (RTP) have attracted great attention for their wide applications in optoelectronic devices and bioimaging. However, these materials are still very scarce, partially due to the unclear mechanism and lack of designing guidelines. Herein we develop seven 10-phenyl-10H-phenothiazine-5,5-dioxide-based derivatives, reveal their different RTP properties and underlying mechanism, and exploit their potential imaging applications. Coupled with the preliminary theoretical calculations, it is found that strong π-π interactions in solid state can promote the persistent RTP. Particularly, CS-CF3 shows the unique photo-induced phosphorescence in response to the changes in molecular packing, further confirming the key influence of the molecular packing on the RTP property. Furthermore, CS-F with its long RTP lifetime could be utilized for real-time excitation-free phosphorescent imaging in living mice. Thus, our study paves the way for the development of persistent RTP materials, in both the practical applications and the inherent mechanism.

Abstract: Friction and wear remain as the primary modes of mechanical energy dissipation in moving mechanical assemblies; thus, it is desirable to minimize friction in a number of applications. We demonstrate that superlubricity can be realized at engineering scale when graphene is used in combination with nanodiamond particles and diamondlike carbon (DLC). Macroscopic superlubricity originates because graphene patches at a sliding interface wrap around nanodiamonds to form nanoscrolls with reduced contact area that slide against the DLC surface, achieving an incommensurate contact and substantially reduced coefficient of friction (~0.004). Atomistic simulations elucidate the overall mechanism and mesoscopic link bridging the nanoscale mechanics and macroscopic experimental observations.

Abstract: Existing zero-shot learning (ZSL) models typically learn a projection function from a feature space to a semantic embedding space (e.g. attribute space). However, such a projection function is only concerned with predicting the training seen class semantic representation (e.g. attribute prediction) or classification. When applied to test data, which in the context of ZSL contains different (unseen) classes without training data, a ZSL model typically suffers from the project domain shift problem. In this work, we present a novel solution to ZSL based on learning a Semantic AutoEncoder (SAE). Taking the encoder-decoder paradigm, an encoder aims to project a visual feature vector into the semantic space as in the existing ZSL models. However, the decoder exerts an additional constraint, that is, the projection/code must be able to reconstruct the original visual feature. We show that with this additional reconstruction constraint, the learned projection function from the seen classes is able to generalise better to the new unseen classes. Importantly, the encoder and decoder are linear and symmetric which enable us to develop an extremely efficient learning algorithm. Extensive experiments on six benchmark datasets demonstrate that the proposed SAE outperforms significantly the existing ZSL models with the additional benefit of lower computational cost. Furthermore, when the SAE is applied to supervised clustering problem, it also beats the state-of-the-art.

Abstract: We present SummaRuNNer, a Recurrent Neural Network (RNN) based sequence model for extractive summarization of documents and show that it achieves performance better than or comparable to state-of-the-art. Our model has the additional advantage of being very interpretable, since it allows visualization of its predictions broken up by abstract features such as information content, salience and novelty. Another novel contribution of our work is abstractive training of our extractive model that can train on human generated reference summaries alone, eliminating the need for sentence-level extractive labels.

Abstract: We present persona-based models for handling the issue of speaker consistency in neural response generation. A speaker model encodes personas in distributed embeddings that capture individual characteristics such as background information and speaking style. A dyadic speaker-addressee model captures properties of interactions between two interlocutors. Our models yield qualitative performance improvements in both perplexity and BLEU scores over baseline sequence-to-sequence models, with similar gains in speaker consistency as measured by human judges.

Abstract: Purpose: Checkpoint inhibitors demonstrate salutary anticancer effects, including long-term remissions. PD-L1 expression/amplification, high mutational burden, and mismatch repair deficiency correlate with response. We have, however, observed a subset of patients who appear to be "hyperprogressors," with a greatly accelerated rate of tumor growth and clinical deterioration compared with pretherapy, which was also recently reported by Institut Gustave Roussy. The current study investigated potential genomic markers associated with "hyperprogression" after immunotherapy.Experimental Design: Consecutive stage IV cancer patients who received immunotherapies (CTLA-4, PD-1/PD-L1 inhibitors or other [investigational] agents) and had their tumor evaluated by next-generation sequencing were analyzed (N = 155). We defined hyperprogression as time-to-treatment failure (TTF) 50% increase in tumor burden compared with preimmunotherapy imaging, and >2-fold increase in progression pace.Results: Amongst 155 patients, TTF <2 months was seen in all six individuals with MDM2/MDM4 amplification. After anti-PD1/PDL1 monotherapy, four of these patients showed remarkable increases in existing tumor size (55% to 258%), new large masses, and significantly accelerated progression pace (2.3-, 7.1-, 7.2- and 42.3-fold compared with the 2 months before immunotherapy). In multivariate analysis, MDM2/MDM4 and EGFR alterations correlated with TTF <2 months. Two of 10 patients with EGFR alterations were also hyperprogressors (53.6% and 125% increase in tumor size; 35.7- and 41.7-fold increase).Conclusions: Some patients with MDM2 family amplification or EGFR aberrations had poor clinical outcome and significantly increased rate of tumor growth after single-agent checkpoint (PD-1/PD-L1) inhibitors. Genomic profiles may help to identify patients at risk for hyperprogression on immunotherapy. Further investigation is urgently needed. Clin Cancer Res; 23(15); 4242-50. ©2017 AACR.

Abstract: Zika virus (ZIKV) has recently emerged as a pandemic associated with severe neuropathology in newborns and adults. There are no ZIKV-specific treatments or preventatives. Therefore, the development of a safe and effective vaccine is a high priority. Messenger RNA (mRNA) has emerged as a versatile and highly effective platform to deliver vaccine antigens and therapeutic proteins. Here we demonstrate that a single low-dose intradermal immunization with lipid-nanoparticle-encapsulated nucleoside-modified mRNA (mRNA-LNP) encoding the pre-membrane and envelope glycoproteins of a strain from the ZIKV outbreak in 2013 elicited potent and durable neutralizing antibody responses in mice and non-human primates. Immunization with 30 μg of nucleoside-modified ZIKV mRNA-LNP protected mice against ZIKV challenges at 2 weeks or 5 months after vaccination, and a single dose of 50 μg was sufficient to protect non-human primates against a challenge at 5 weeks after vaccination. These data demonstrate that nucleoside-modified mRNA-LNP elicits rapid and durable protective immunity and therefore represents a new and promising vaccine candidate for the global fight against ZIKV.

Abstract: Summary Background Deletion of chromosome 17p (del[17p]) in patients with chronic lymphocytic leukaemia confers very poor prognosis when treated with standard chemo-immunotherapy. Venetoclax is an oral small-molecule BCL2 inhibitor that induces chronic lymphocytic leukaemia cell apoptosis. In a previous first-in-human study of venetoclax, 77% of patients with relapsed or refractory chronic lymphocytic leukaemia achieved an overall response. Here we aimed to assess the activity and safety of venetoclax monotherapy in patients with relapsed or refractory del(17p) chronic lymphocytic leukaemia. Methods In this phase 2, single-arm, multicentre study, we recruited patients aged 18 years and older with del(17p) relapsed or refractory chronic lymphocytic leukaemia (as defined by 2008 Modified International Workshop on Chronic Lymphocytic Leukemia guidelines) from 31 centres in the USA, Canada, UK, Germany, Poland, and Australia. Patients started once daily venetoclax with a weekly dose ramp-up schedule (20, 50, 100, 200, 400 mg) over 4–5 weeks. Patients were then given daily 400 mg continuous dosing until disease progression or discontinuation for another reason. The primary endpoint was the proportion of patients achieving an overall response, assessed by an independent review committee. Activity and safety analyses included all patients who received at least one dose of study drug (per protocol). This study is registered with ClinicalTrials.gov, number NCT01889186. Follow-up is ongoing, and patients are still receiving treatment. Findings Between May 27, 2013, and June 27, 2014, 107 patients were enrolled into the study. At a median follow-up of 12·1 months (IQR 10·1–14·2), an overall response by independent review was achieved in 85 (79·4%; 95% CI 70·5–86·6) of 107 patients. The most common grade 3–4 adverse events were neutropenia (43 [40%]), infection (21 [20%]), anaemia (19 [18%]), and thrombocytopenia (16 [15%]). Serious adverse events occurred in 59 (55%) patients, irrespective of their relationship to treatment, with the most common (≥5% of patients) being pyrexia and autoimmune haemolytic anaemia (seven [7%] each), pneumonia (six [6%]), and febrile neutropenia (five [5%]). 11 patients died in the study within 30 days of the last dose of venetoclax; seven due to disease progression and four from an adverse event (none assessed as treatment related). Interpretation Results of this trial show that venetoclax monotherapy is active and well tolerated in patients with relapsed or refractory del(17p) chronic lymphocytic leukaemia, providing a new therapeutic option for this very poor prognosis population. Additionally, in view of the distinct mechanism-of-action of venetoclax, combinations or sequencing with other novel targeted agents should be investigated to further advance treatment of del(17p) chronic lymphocytic leukaemia. Funding AbbVie and Genentech.

Abstract: Python has become the programming language of choice for research and industry projects related to data science, machine learning, and deep learning. Since optimization is an inherent part of these research fields, more optimization related frameworks have arisen in the past few years. Only a few of them support optimization of multiple conflicting objectives at a time, but do not provide comprehensive tools for a complete multi-objective optimization task. To address this issue, we have developed pymoo, a multi-objective optimization framework in Python. We provide a guide to getting started with our framework by demonstrating the implementation of an exemplary constrained multi-objective optimization scenario. Moreover, we give a high-level overview of the architecture of pymoo to show its capabilities followed by an explanation of each module and its corresponding sub-modules. The implementations in our framework are customizable and algorithms can be modified/extended by supplying custom operators. Moreover, a variety of single, multi- and many-objective test problems are provided and gradients can be retrieved by automatic differentiation out of the box. Also, pymoo addresses practical needs, such as the parallelization of function evaluations, methods to visualize low and high-dimensional spaces, and tools for multi-criteria decision making. For more information about pymoo, readers are encouraged to visit: https://pymoo.org.

Abstract: We present reduced data and data products from the 3D-HST survey, a 248-orbit HST Treasury program. The survey obtained WFC3 G141 grism spectroscopy in four of the five CANDELS fields: AEGIS, COSMOS, GOODS-S, and UDS, along with WFC3 $H_{140}$ imaging, parallel ACS G800L spectroscopy, and parallel $I_{814}$ imaging. In a previous paper (Skelton et al. 2014) we presented photometric catalogs in these four fields and in GOODS-N, the fifth CANDELS field. Here we describe and present the WFC3 G141 spectroscopic data, again augmented with data from GO-1600 in GOODS-N. The data analysis is complicated by the fact that no slits are used: all objects in the WFC3 field are dispersed, and many spectra overlap. We developed software to automatically and optimally extract interlaced 2D and 1D spectra for all objects in the Skelton et al. (2014) photometric catalogs. The 2D spectra and the multi-band photometry were fit simultaneously to determine redshifts and emission line strengths, taking the morphology of the galaxies explicitly into account. The resulting catalog has 98,663 measured redshifts and line strengths down to $JH_{IR}\leq 26$ and 22,548 with $JH_{IR}\leq 24$, where we comfortably detect continuum emission. Of this sample 5,459 galaxies are at $z>1.5$ and 9,621 are at $0.7