Search for serendipitous TNO occultation in X-rays

To study the population properties of small, remote objects beyond Neptune's orbit in the outer solar system, of kilometer size or smaller, serendipitous occultation search is so far the only way. For hectometer-sized Trans-Neptunian Objects (TNOs), optical shadows actually disappear because of diffraction. Observations at shorter wave lengths are needed. Here we report the effort of TNO occultation search in X-rays using RXTE/PCA data of Sco X-1 taken from June 2007 to October 2011. No definite TNO occultation events were found in the 334 ks data. We investigate the detection efficiency dependence on the TNO size to better define the sensible size range of our approach and suggest upper limits to the TNO size distribution in the size range from 30 m to 300 m. A list of X-ray sources suitable for future larger facilities to observe is proposed.Comment: Accepted to publish in MNRA

MECHANISM OF ORLISTAT HYDROLYSIS BY FATTY ACID SYNTHASE THIOESTERASE

poster abstractFatty acid synthase (FASN) is the sole protein capable of de novo synthesis of free fatty acids. The fatty acid synthesis cycle begins with the condensa-tion of acetyl-CoA and malonyl-CoA, and continues with the elongation of the fatty acid chain, which is tethered to an acyl carrier protein domain (ACP), via a repeating cycle. At the end of elongation, the thioesterase (TE) domain of FASN cleaves the bond between the fatty acid and ACP, releasing the fatty acid. FASN has been found to be over-expressed in a wide variety of human cancers, and this over-expression is correlated to a higher meta-static potential and poorer prognosis in cancer patients. Orlistat, an FDA ap-proved drug for obesity treatment, is a compound found to reversibly inhibit FASN TE by covalently binding to the active site serine within the TE domain. In crystal structure studies, a hydrolyzed form of orlistat can also be ob-served in the active site of TE, demonstrating that orlistat is not a stable in-hibitor of FASN. In this study, we examined the mechanism of orlistat hy-drolysis within the TE domain of FASN using molecular dynamics simula-tions. We found that the hexanoyl tail of orlistat is capable of shifting while covalently bound to the active site serine, and that this shift is accompanied by the destabilization of a hydrogen bond that exists between a hydroxyl moiety of orlistat and the active site histidine, allowing a catalytic water molecule to enter the active site with the proper orientation for catalysis of the covalent bond between orlistat and serine. These findings suggest that the hexanoyl tail of orlistat plays an important role in its hydrolysis and may guide the future design of new inhibitors that target the TE domain of FASN with greater endurance for potential use in the treatment of cancer

Diethyl 2,2′-[(5-dimethyl­amino-1-naphth­yl)sulfonyl­imino]diacetate

In the title compound, C20H26N2O6S, the N atom of the dimethyl­amino group is displaced by 0.113 (2) Å from the plane of the naphthalene ring system. The two eth­oxy groups adopt zigzag conformations. In the crystal structure, weak inter­molecular C—H⋯O hydrogen bonds link the mol­ecules, forming a three-dimensional network. Both ethyl groups are disordered over two sites with the ratios of refined occupancies being 0.857 (16):0.143 (16) and 0.517 (14):0.483 (14)

Long-term Blood Pressure Prediction with Deep Recurrent Neural Networks

Existing methods for arterial blood pressure (BP) estimation directly map the input physiological signals to output BP values without explicitly modeling the underlying temporal dependencies in BP dynamics. As a result, these models suffer from accuracy decay over a long time and thus require frequent calibration. In this work, we address this issue by formulating BP estimation as a sequence prediction problem in which both the input and target are temporal sequences. We propose a novel deep recurrent neural network (RNN) consisting of multilayered Long Short-Term Memory (LSTM) networks, which are incorporated with (1) a bidirectional structure to access larger-scale context information of input sequence, and (2) residual connections to allow gradients in deep RNN to propagate more effectively. The proposed deep RNN model was tested on a static BP dataset, and it achieved root mean square error (RMSE) of 3.90 and 2.66 mmHg for systolic BP (SBP) and diastolic BP (DBP) prediction respectively, surpassing the accuracy of traditional BP prediction models. On a multi-day BP dataset, the deep RNN achieved RMSE of 3.84, 5.25, 5.80 and 5.81 mmHg for the 1st day, 2nd day, 4th day and 6th month after the 1st day SBP prediction, and 1.80, 4.78, 5.0, 5.21 mmHg for corresponding DBP prediction, respectively, which outperforms all previous models with notable improvement. The experimental results suggest that modeling the temporal dependencies in BP dynamics significantly improves the long-term BP prediction accuracy.Comment: To appear in IEEE BHI 201

Effective Control of Bioelectricity Generation from a Microbial Fuel Cell by Logical Combinations of pH and Temperature

In this study, a microbial fuel cell (MFC) with switchable power release is designed, which can be logically controlled by combinations of the most physiologically important parameters such as “temperature” and “pH.” Changes in voltage output in response to temperature and pH changes were significant in which voltage output decreased sharply when temperature was lowered from 30°C to 10°C or pH was decreased from 7.0 to 5.0. The switchability of the MFC comes from the microbial anode whose activity is affected by the combined medium temperature and pH. Changes in temperature and pH cause reversible activation-inactivation of the bioanode, thus affecting the activity of the entire MFC. With temperature and pH as input signals, an AND logic operation is constructed for the MFC whose power density is controlled. The developed system has the potential to meet the requirement of power supplies producing electrical power on-demand for self-powered biosensors or biomedical devices

Effective Control of Bioelectricity Generation from a Microbial Fuel Cell by Logical Combinations of pH and Temperature

In this study, a microbial fuel cell (MFC) with switchable power release is designed, which can be logically controlled by combinations of the most physiologically important parameters such as “temperature” and “pH.” Changes in voltage output in response to temperature and pH changes were significant in which voltage output decreased sharply when temperature was lowered from 30°C to 10°C or pH was decreased from 7.0 to 5.0. The switchability of the MFC comes from the microbial anode whose activity is affected by the combined medium temperature and pH. Changes in temperature and pH cause reversible activation-inactivation of the bioanode, thus affecting the activity of the entire MFC. With temperature and pH as input signals, an AND logic operation is constructed for the MFC whose power density is controlled. The developed system has the potential to meet the requirement of power supplies producing electrical power on-demand for self-powered biosensors or biomedical devices

Two decades of research in discovery of anticancer drugs targeting STAT3, how close are we?

Signal transducer and activator of transcription 3 (STAT3) controls many biological processes including differentiation, survival, proliferation, and angiogenesis. In normal healthy cells, STAT3 is tightly regulated to maintain a momentary active state. However, aberrant or constitutively activated STAT3 has been observed in many different cancers and constitutively activated STAT3 has been shown to associate with poor prognosis and tumor progression. For this reason, STAT3 has been studied as a possible target in the treatment of many different types of cancers. However, despite decades of research, a FDA-approved STAT3 inhibitor has yet to emerge. In this review, we will analyze past studies targeting STAT3 for drug discovery, understand possible causes of failure in these studies, and provide potential insights for future efforts to overcome these roadblocks