11 research outputs found
Matrix Completion When Missing Is Not at Random and Its Applications in Causal Panel Data Models
This paper develops an inferential framework for matrix completion when
missing is not at random and without the requirement of strong signals. Our
development is based on the observation that if the number of missing entries
is small enough compared to the panel size, then they can be estimated well
even when missing is not at random. Taking advantage of this fact, we divide
the missing entries into smaller groups and estimate each group via nuclear
norm regularization. In addition, we show that with appropriate debiasing, our
proposed estimate is asymptotically normal even for fairly weak signals. Our
work is motivated by recent research on the Tick Size Pilot Program, an
experiment conducted by the Security and Exchange Commission (SEC) to evaluate
the impact of widening the tick size on the market quality of stocks from 2016
to 2018. While previous studies were based on traditional regression or
difference-in-difference methods by assuming that the treatment effect is
invariant with respect to time and unit, our analyses suggest significant
heterogeneity across units and intriguing dynamics over time during the pilot
program
Inference for Low-rank Models without Estimating the Rank
This paper studies the inference about linear functionals of high-dimensional
low-rank matrices. While most existing inference methods would require
consistent estimation of the true rank, our procedure is robust to rank
misspecification, making it a promising approach in applications where rank
estimation can be unreliable. We estimate the low-rank spaces using
pre-specified weighting matrices, known as diversified projections. A novel
statistical insight is that, unlike the usual statistical wisdom that
overfitting mainly introduces additional variances, the over-estimated low-rank
space also gives rise to a non-negligible bias due to an implicit ridge-type
regularization. We develop a new inference procedure and show that the central
limit theorem holds as long as the pre-specified rank is no smaller than the
true rank. Empirically, we apply our method to the U.S. federal grants
allocation data and test the existence of pork-barrel politics
Inference for Low-rank Completion without Sample Splitting with Application to Treatment Effect Estimation
This paper studies the inferential theory for estimating low-rank matrices.
It also provides an inference method for the average treatment effect as an
application. We show that the least square estimation of eigenvectors following
the nuclear norm penalization attains the asymptotic normality. The key
contribution of our method is that it does not require sample splitting. In
addition, this paper allows dependent observation patterns and heterogeneous
observation probabilities. Empirically, we apply the proposed procedure to
estimating the impact of the presidential vote on allocating the U.S. federal
budget to the states
Discovery of Q203, a potent clinical candidate for the treatment of tuberculosis
New therapeutic strategies are needed to combat the tuberculosis pandemic and the spread of multidrug-resistant (MDR) and extensively drug-resistant (XDR) forms of the disease, which remain a serious public health challenge worldwide1, 2. The most urgent clinical need is to discover potent agents capable of reducing the duration of MDR and XDR tuberculosis therapy with a success rate comparable to that of current therapies for drug-susceptible tuberculosis. The last decade has seen the discovery of new agent classes for the management of tuberculosis3, 4, 5, several of which are currently in clinical trials6, 7, 8. However, given the high attrition rate of drug candidates during clinical development and the emergence of drug resistance, the discovery of additional clinical candidates is clearly needed. Here, we report on a promising class of imidazopyridine amide (IPA) compounds that block Mycobacterium tuberculosis growth by targeting the respiratory cytochrome bc1 complex. The optimized IPA compound Q203 inhibited the growth of MDR and XDR M. tuberculosis clinical isolates in culture broth medium in the low nanomolar range and was efficacious in a mouse model of tuberculosis at a dose less than 1 mg per kg body weight, which highlights the potency of this compound. In addition, Q203 displays pharmacokinetic and safety profiles compatible with once-daily dosing. Together, our data indicate that Q203 is a promising new clinical candidate for the treatment of tuberculosis
Lead Optimization of a Novel Series of Imidazo[1,2‑<i>a</i>]pyridine Amides Leading to a Clinical Candidate (Q203) as a Multi- and Extensively-Drug-Resistant Anti-tuberculosis Agent
A critical
unmet clinical need to combat the global tuberculosis
epidemic is the development of potent agents capable of reducing the
time of multi-drug-resistant (MDR) and extensively-drug-resistant
(XDR) tuberculosis therapy. In this paper, we report on the optimization
of imidazoÂ[1,2-<i>a</i>]Âpyridine amide (IPA) lead compound <b>1</b>, which led to the design and synthesis of Q203 (<b>50</b>). We found that the amide linker with IPA core is very important
for activity against Mycobacterium tuberculosis H37Rv. Linearity and lipophilicity of the amine part in the IPA
series play a critical role in improving in vitro and in vivo efficacy
and pharmacokinetic profile. The optimized IPAs <b>49</b> and <b>50</b> showed not only excellent oral bioavailability (80.2% and
90.7%, respectively) with high exposure of the area under curve (AUC)
but also displayed significant colony-forming unit (CFU) reduction
(1.52 and 3.13 log<sub>10</sub> reduction at 10 mg/kg dosing level,
respectively) in mouse lung
Discovery of Q203, a potent clinical candidate for the treatment of tuberculosis.
New prophylactic and therapeutic strategies are needed to combat the tuberculosis pandemic and the spread of extensively-drug resistant form of the disease. During the course of a high-content chemical screen, ImidazoPyridine Amides (IPA) were identified as a promising class of anti-tubercular agents. The optimized IPA compound Q203 inhibits the growth of multi- and extensively-drug resistant clinical isolates of M. tuberculosis in the low nanomolar range. Q203 was efficacious in vivo at a dose below 1mg/kg, making this compound one of the most potent discovered up to date. In addition, it shows pharmacokinetic and safety profiles compatible with once daily dosing. A reverse genetic approach identifies the ubiquinol cytochrome C reductase (QcrB, Rv2196) as the target of Q203. Mode of action studies revealed that Q203 inhibits the process of ATP synthesis in both replicating and hypoxic non-replicating M. tuberculosis. Altogether, our data indicates that Q203 is a promising clinical candidate for the treatment of tuberculosis