The Chinese University of Hong Kong

About: The Chinese University of Hong Kong is a education organization based out in Hong Kong, China. It is known for research contribution in the topics: Population & Computer science. The organization has 43411 authors who have published 93672 publications receiving 3066651 citations.

GW190521: A Binary Black Hole Merger with a Total Mass of 150 M

Abstract: On May 21, 2019 at 03:02:29 UTC Advanced LIGO and Advanced Virgo observed a short duration gravitational-wave signal, GW190521, with a three-detector network signal-to-noise ratio of 14.7, and an estimated false-alarm rate of 1 in 4900 yr using a search sensitive to generic transients. If GW190521 is from a quasicircular binary inspiral, then the detected signal is consistent with the merger of two black holes with masses of 85_{-14}^{+21} M_{⊙} and 66_{-18}^{+17} M_{⊙} (90% credible intervals). We infer that the primary black hole mass lies within the gap produced by (pulsational) pair-instability supernova processes, with only a 0.32% probability of being below 65 M_{⊙}. We calculate the mass of the remnant to be 142_{-16}^{+28} M_{⊙}, which can be considered an intermediate mass black hole (IMBH). The luminosity distance of the source is 5.3_{-2.6}^{+2.4} Gpc, corresponding to a redshift of 0.82_{-0.34}^{+0.28}. The inferred rate of mergers similar to GW190521 is 0.13_{-0.11}^{+0.30} Gpc^{-3} yr^{-1}.

Drug-Resistant Epilepsy

Abstract: Nearly a quarter of patients with seizures have drug-resistant epilepsy. This review examines how this diagnosis should be established and how to recognize pseudoresistance. It explains possible mechanisms of drug-resistant epilepsy and presents treatment strategies.

Learning Deep Representation for Imbalanced Classification

Abstract: Data in vision domain often exhibit highly-skewed class distribution, i.e., most data belong to a few majority classes, while the minority classes only contain a scarce amount of instances. To mitigate this issue, contemporary classification methods based on deep convolutional neural network (CNN) typically follow classic strategies such as class re-sampling or cost-sensitive training. In this paper, we conduct extensive and systematic experiments to validate the effectiveness of these classic schemes for representation learning on class-imbalanced data. We further demonstrate that more discriminative deep representation can be learned by enforcing a deep network to maintain both intercluster and inter-class margins. This tighter constraint effectively reduces the class imbalance inherent in the local data neighborhood. We show that the margins can be easily deployed in standard deep learning framework through quintuplet instance sampling and the associated triple-header hinge loss. The representation learned by our approach, when combined with a simple k-nearest neighbor (kNN) algorithm, shows significant improvements over existing methods on both high-and low-level vision classification tasks that exhibit imbalanced class distribution.

Entrepreneurship in Emerging Economies: Where Are We Today and Where Should the Research Go in the Future

Abstract: Emerging economies are characterized by an increasing market orientation and an expanding economic foundation. The success of many of these economies is such that they are rapidly becoming major economic forces in the world. Entrepreneurship plays a key role in this economic development. Yet to date, little is known about entrepreneurship in emerging economies. This introductory article to the special issue on entrepreneurship in emerging economies examines the literature that exists to date in this important domain. It then reviews the research that was generated as part of this special issue on this topic. The article concludes with a discussion of the critical future research needs in this area.

Entrectinib in patients with advanced or metastatic NTRK fusion-positive solid tumours: integrated analysis of three phase 1–2 trials

Abstract: Summary Background Entrectinib is a potent inhibitor of tropomyosin receptor kinase (TRK) A, B, and C, which has been shown to have anti-tumour activity against NTRK gene fusion-positive solid tumours, including CNS activity due to its ability to penetrate the blood–brain barrier. We present an integrated efficacy and safety analysis of patients with metastatic or locally advanced solid tumours harbouring oncogenic NTRK1, NTRK2, and NTRK3 gene fusions treated in three ongoing, early-phase trials. Methods An integrated database comprised the pivotal datasets of three, ongoing phase 1 or 2 clinical trials (ALKA-372-001, STARTRK-1, and STARTRK-2), which enrolled patients aged 18 years or older with metastatic or locally advanced NTRK fusion-positive solid tumours who received entrectinib orally at a dose of at least 600 mg once per day in a capsule. All patients had an Eastern Cooperative Oncology Group performance status of 0–2 and could have received previous anti-cancer therapy (except previous TRK inhibitors). The primary endpoints, the proportion of patients with an objective response and median duration of response, were evaluated by blinded independent central review in the efficacy-evaluable population (ie, patients with NTRK fusion-positive solid tumours who were TRK inhibitor-naive and had received at least one dose of entrectinib). Overall safety evaluable population included patients from STARTRK-1, STARTRK-2, ALKA-372-001, and STARTRK-NG ( NCT02650401 ; treating young adult and paediatric patients [aged ≤21 years]), who received at least one dose of entrectinib, regardless of tumour type or gene rearrangement. NTRK fusion-positive safety evaluable population comprised all patients who have received at least one dose of entrectinib regardless of dose or follow-up. These ongoing studies are registered with ClinicalTrials.gov , NCT02097810 (STARTRK-1) and NCT02568267 (STARTRK-2), and EudraCT, 2012–000148–88 (ALKA-372-001). Findings Patients were enrolled in ALKA-372–001 from Oct 26, 2012, to March 27, 2018; in STARTRK-1 from Aug 7, 2014, to May 10, 2018; and in STARTRK-2 from Nov 19, 2015 (enrolment is ongoing). At the data cutoff date for this analysis (May 31, 2018) the efficacy-evaluable population comprised 54 adults with advanced or metastatic NTRK fusion-positive solid tumours comprising ten different tumour types and 19 different histologies. Median follow-up was 12.9 months (IQR 8·77–18·76). 31 (57%; 95% CI 43·2–70·8) of 54 patients had an objective response, of which four (7%) were complete responses and 27 (50%) partial reponses. Median duration of response was 10 months (95% CI 7·1 to not estimable). The most common grade 3 or 4 treatment-related adverse events in both safety populations were increased weight (seven [10%] of 68 patients in the NTRK fusion-positive safety population and in 18 [5%] of 355 patients in the overall safety-evaluable population) and anaemia (8 [12%] and 16 [5%]). The most common serious treatment-related adverse events were nervous system disorders (three [4%] of 68 patients and ten [3%] of 355 patients). No treatment-related deaths occurred. Interpretation Entrectinib induced durable and clinically meaningful responses in patients with NTRK fusion-positive solid tumours, and was well tolerated with a manageable safety profile. These results show that entrectinib is a safe and active treatment option for patients with NTRK fusion-positive solid tumours. These data highlight the need to routinely test for NTRK fusions to broaden the therapeutic options available for patients with NTRK fusion-positive solid tumours. Funding Ignyta/F Hoffmann-La Roche.