123 research outputs found
Part A: Antimalarial agents modified at the C-16 position of artemisinin; Part B: Lead optimization of falcipain-2 and falcipain-3 inhibitors
Part A: Antimalarial Agents modified at the C-16 position of Artemisinin. Malaria is a widespread tropical and subtropical parasitic disease which is caused by malarial parasites and transmitted by the infected anopheles mosquitoe. The natural product artemisinin and its derivatives are currently considered the most effective drugs against drug resistant plasmodium falciparum. However, its undesired physicochemical proprieties have limited its usage. In order to improve its effectiveness, scientists around the world have developed novel methodology to synthesize artemisinin derivatives on different positions of the artemisinin skeleton. Previous work in our group has shown that many analogues modified at the C-16 of artemisinin had improved efficacy along with modified physicochemical proprieties. This work focuses on the synthesis of heteroatomic and heterocyclic derivatives of artemisinin with the emphasis on C-16 substituted triazole containing side-chains. Successful synthetic results and subsequent bioassay demonstrated that the compounds have modest antimalarial activity compared to artemisinin and improved water solubility. With these encouraging results in hand, further work is underway to tune the desired physicochemical properties so that plasma half-life and oral bioavailability will be improved. Part B: Lead Optimization of Falcipain-2 and Falcipain-3 Inhibitors. The expanding usage of artemisinin combination therapy casts concern about the potential development of drug resistance to this drug family, thus the search for new drug targets is always needed. Falcipain-2 (FP-II) and falcipain-3 (FP-III) are two cysteine proteases which malarial parasites utilize to degrade hemoglobin to obtain amino acids essential to the parasite. The inhibition of these two enzymes has been shown to have deadly effects on the protozoan life cycle. Recently published crystal structures of FP-II provided an outstanding opportunity for rational drug design and discovery. In the present study, structure-based optimization of virtual screening hits was carried out using scaffold hopping, docking and analogue synthesis. Unfortunately, the biological evaluation of the synthesized compounds against FP-II and FP-III indicated these compounds are inactive. However, the information gained from this exercise could aid further in optimization of this series of compounds
Impact of Limited Statistics on the Measured Hyper-Order Cumulants of Net-Proton Distributions in Heavy-Ion Collisions
Hyper-order cumulants and of net-baryon distributions are
anticipated to offer crucial insights into the phase transition from
quark-gluon plasma to hadronic matter in heavy-ion collisions. However, the
accuracy of and is highly contingent on the fine shape of the
distribution's tail, the detectable range of which could be essentially
truncated by low statistics. In this paper, we use the fast Skellam-based
simulations, as well as the Ultrarelativistic Quantum Molecular Dynamics model,
to assess the impact of limited statistics on the measurements of and
of net-proton distributions at lower RHIC energies. Both ratios
decrease from the unity baseline as we reduce statistics, and could even turn
negative without a pertinent physics mechanism. By incorporating statistics
akin to experimental data, we can replicate the net-proton and
values comparable to the corresponding measurements for Au+Au
collisions at 7.7, 11.5 and 14.5 GeV. Our findings underscore
a caveat to the interpretation of the observed beam energy dependence of
hyper-order cumulants.Comment: 6 pages, 7 figure
Context-aware Event Forecasting via Graph Disentanglement
Event forecasting has been a demanding and challenging task throughout the
entire human history. It plays a pivotal role in crisis alarming and disaster
prevention in various aspects of the whole society. The task of event
forecasting aims to model the relational and temporal patterns based on
historical events and makes forecasting to what will happen in the future. Most
existing studies on event forecasting formulate it as a problem of link
prediction on temporal event graphs. However, such pure structured formulation
suffers from two main limitations: 1) most events fall into general and
high-level types in the event ontology, and therefore they tend to be
coarse-grained and offers little utility which inevitably harms the forecasting
accuracy; and 2) the events defined by a fixed ontology are unable to retain
the out-of-ontology contextual information. To address these limitations, we
propose a novel task of context-aware event forecasting which incorporates
auxiliary contextual information. First, the categorical context provides
supplementary fine-grained information to the coarse-grained events. Second and
more importantly, the context provides additional information towards specific
situation and condition, which is crucial or even determinant to what will
happen next. However, it is challenging to properly integrate context into the
event forecasting framework, considering the complex patterns in the
multi-context scenario. Towards this end, we design a novel framework named
Separation and Collaboration Graph Disentanglement (short as SeCoGD) for
context-aware event forecasting. Since there is no available dataset for this
novel task, we construct three large-scale datasets based on GDELT.
Experimental results demonstrate that our model outperforms a list of SOTA
methods.Comment: KDD 2023, 9 pages, 7 figures, 4 table
Fine-grained Data Distribution Alignment for Post-Training Quantization
While post-training quantization receives popularity mostly due to its
evasion in accessing the original complete training dataset, its poor
performance also stems from scarce images. To alleviate this limitation, in
this paper, we leverage the synthetic data introduced by zero-shot quantization
with calibration dataset and propose a fine-grained data distribution alignment
(FDDA) method to boost the performance of post-training quantization. The
method is based on two important properties of batch normalization statistics
(BNS) we observed in deep layers of the trained network, (i.e.), inter-class
separation and intra-class incohesion. To preserve this fine-grained
distribution information: 1) We calculate the per-class BNS of the calibration
dataset as the BNS centers of each class and propose a BNS-centralized loss to
force the synthetic data distributions of different classes to be close to
their own centers. 2) We add Gaussian noise into the centers to imitate the
incohesion and propose a BNS-distorted loss to force the synthetic data
distribution of the same class to be close to the distorted centers. By
utilizing these two fine-grained losses, our method manifests the
state-of-the-art performance on ImageNet, especially when both the first and
last layers are quantized to the low-bit. Code is at
\url{https://github.com/zysxmu/FDDA}.Comment: ECCV202
7ÎČ-HydroxyÂartemisinin
Crystals of the title compound [systematic name: (3R,6R,7S,8aR,9R,12aR)-7-hydrÂoxy-3,6,9-trimethylÂoctaÂhydro-3,12-epÂoxy[1,2]dioxepino[4,3-i]isochromen-10(3H)-one], C15H22O6, were obtained from microbial transformation of artemisinin by a culture of Cunninghamella elegans. The stereochemistry of the compound is consistent with the spectroscopic findings in previously published works. A weak OâHâŻO hydrogen bond occurs in the crystal structure, together with intermolecular CâHâŻO hydrogen bonds
Ultralow thermal conductivity of single crystalline porous silicon nanowires
Porous materials provide a large surface to volume ratio, thereby providing a
knob to alter fundamental properties in unprecedented ways. In thermal
transport, porous nanomaterials can reduce thermal conductivity by not only
enhancing phonon scattering from the boundaries of the pores and therefore
decreasing the phonon mean free path, but also by reducing the phonon group
velocity. Here we establish a structure-property relationship by measuring the
porosity and thermal conductivity of individual electrolessly etched single
crystalline silicon nanowires using a novel electron beam heating technique.
Such porous silicon nanowires exhibit extremely low diffusive thermal
conductivity (as low as 0.33 Wm-1K-1 at 300K for 43% porosity), even lower than
that of amorphous silicon. The origin of such ultralow thermal conductivity is
understood as a reduction in the phonon group velocity, experimentally verified
by measuring the Young modulus, as well as the smallest structural size ever
reported in crystalline Silicon (less than 5nm). Molecular dynamics simulations
support the observation of a drastic reduction in thermal conductivity of
silicon nanowires as a function of porosity. Such porous materials provide an
intriguing platform to tune phonon transport, which can be useful in the design
of functional materials towards electronics and nano-electromechanical systems
Reply to: Mobility overestimation in MoS transistors due to invasive voltage probes
In this reply, we include new experimental results and verify that the
observed non-linearity in rippled-MoS (leading to mobility kink) is an
intrinsic property of a disordered system, rather than contact effects
(invasive probes) or other device issues. Noting that Peng Wu's hypothesis is
based on a highly ordered ideal system, transfer curves are expected to be
linear, and the carrier density is assumed be constant. Wu's model is therefore
oversimplified for disordered systems and neglects carrier-density dependent
scattering physics. Thus, it is fundamentally incompatible with our
rippled-MoS, and leads to the wrong conclusion
- âŠ