Miracle-dz/UniFAR-FTUs huggingface dataset:
Hsp90 interacts with inducible NO synthase client protein in its heme‐free state and then drives heme insertion by an ATP‐dependent process

Maturation of NOS enzymes requires that they incorporate heme to become active, but how this cellular process occurs is unclear. We investigated a role for chaperone heat shock protein 90 (hsp90) in enabling heme insertion into the cytokine‐inducible mouse NOS. We used macrophage cell line RAW 264.7 and human embryonic kidney HEK293T cells and studied insertion of native heme during iNOS expression and insertion of exogenous heme into preformed apo‐iNOS. Pulldown experiments showed that the hsp90‐iNOS complex was present in cells, but the extent of their association was inversely related to iNOS heme content. Hsp90 was primarily associated with apo‐iNOS monomer and was associated 11‐fold less with heme‐containing iNOS monomer or dimer in cells. Kinetic studies showed that hsp90 dissociation occurred coincident with cellular heme insertion into apo‐iNOS (0.8 h–1). The hsp90 inhibitor radicicol or coexpression of an ATPase‐defective hsp90 blocked heme insertion into apo‐iNOS by 90 and 75%, respectively. The ATPase activity of hsp90 was not required for complex formation with iNOS but was essential for heme insertion to occur. We conclude that hsp90 plays a primary role in maturation of iNOS protein by interacting with the apoenzyme in cells and then driving heme insertion in an ATP‐dependent manner. —Ghosh, A. , Chawla‐Sarkar, M. , Stuehr, D. J. Hsp90 interacts with inducible NO synthase client protein in its heme‐free state and then drives heme insertion by an ATP‐dependent process. FASEB J. 25, 2049‐2060 (2011). www.fasebj. org
8.2 A Continuous-Input-Current Passive-Stacked Third-Order Buck Converter Achieving 0.7W/mm2 Power Density and 94% Peak Efficiency

The power density and efficiency of power-management integrated circuits (PMICs) is playing an increasingly important role in the miniaturization of modern computing platforms. Small inductors can be used to help miniaturize buck DCDC converters, however as noted in Fig. 8.2. 1 (lower left), small inductors tend to have high DC resistance (DCR) – greater than a comparably-sized CMOS switch – such that they ultimately limit the achievable power density of miniaturized buck converters to $\lt 0.4 W/mm^{2}$ when including the area of both the passives and the PMIC [1, 2]. While recent work in switched-capacitor (SC) converters has shown that high power density is achievable, it is only possible when employing exotic ultra-high-density capacitors and when operating over a small number of conversion ratios; increasing the number of ratios to support the needs of dynamic voltage scaling loads (e.g., 0.4 to 1.2V) alongside use of conventional capacitor technologies degrades power density to <<0.1 W/mm2 [3]. Hybrid converters, which process power with both capacitors and inductors, offer an attractive means to potentially increase power density and/or efficiency. Resonating an SC circuit with an inductor can, for example, dramatically increase power density to 0.9W/mm2 in [4], however with limited, though improved in [4], ability to regulate beyond the nominal SC ratio at high efficiency. Hybrid multilevel converters can regulate to arbitrary output voltages, though still achieve power density <0.3 W/mm2, [5], in part due to the increased number of passives and associated higher routing complexity, and in part due to the large conversion ratio in the design in [5].
The BosR regulatory protein of Borrelia burgdorferi interfaces with the RpoS regulatory pathway and modulates both the oxidative stress response and pathogenic properties of the Lyme disease spirochete

Borrelia burgdorferi, the Lyme disease spirochete, adapts as it moves between the arthropod and mammalian hosts that it infects. We hypothesize that BosR serves as a global regulator in B. burgdorferi to modulate the oxidative stress response and adapt to mammalian hosts. To test this hypothesis, a bosR mutant in a low‐passage B. burgdorferi isolate was constructed. The resulting bosR::kanR strain was altered when grown microaerobically or anaerobically suggesting that BosR is required for optimal replication under both growth conditions. The absence of BosR increased the sensitivity of B. burgdorferi to hydrogen peroxide and reduced the synthesis of Cdr and NapA, proteins important for cellular redox balance and the oxidative stress response, respectively, suggesting an important role for BosR in borrelial oxidative homeostasis. For the bosR mutant, the production of RpoS was abrogated and resulted in the loss of OspC and DbpA, suggesting that BosR interfaces with the Rrp2–RpoN–RpoS regulatory cascade. Consistent with the linkage to RpoS, cells lacking bosR were non‐infectious in the mouse model of infection. These results indicate that BosR is required for resistance to oxidative stressors and provides a regulatory response that is necessary for B. burgdorferi pathogenesis.
Fake Colorized Image Detection with Channel-wise Convolution based Deep-learning Framework

Colorization is one remarkable emerging image manipulating technique, which maybe potentially used for illegal purpose. In this paper, we introduce WISERNet (Wider Separate-then-reunion Network), a recently proposed deep-learning based data-driven color image steganalyzer in the field of fake colorized image detection. We believe that statistical inconsistencies introduced by different automatic colorization methods can be captured by advanced deep-learning based data-driven color-image steganalyzers such as WISERNet. Experimental evidences reported in this paper supports our claims: the detection performance of our proposed detector obviously outperforms FCID-HIST and FCID-FE, two state-of-the-art hand-crafted features specific to fake colorized image detection. Please note that in our approach we have never explicitly utilized information from the specific channels other than the ordinary red, green, and blue color channel, which is completely different from prior works in this field.
Effects of Vocoder Processing on Speech Perception in Reverberant Classrooms

Channel vocoders have been widely used as acoustic models for current vocoder-centric cochlear implant (CI) signal processing strategies. Previous studies found that 4- to 8-channel vocoded speech in normal hearing (NH) subjects can derive comparable recognition scores to CI subjects who may have 8 to 22 frequency channels. The reasons for this overestimation might include 1) classic vocoders preserve natural intensity dynamic range within the envelopes rather than the much narrower range in CIs and 2) classic vocoders cannot simulate the effect of electric pulse rate. This study presents a novel vocoder based on a direct electric-pulse to acoustic-pulse mapping (i.e., electrodogram to spectrogram) to deal with the overestimation problem. The effects of the proposed vocoders with 22 and 16 channels on speech intelligibility in two real-measured classrooms were tested in NH listeners. Results showed that the proposed vocoders are more sensitive to changes of reverberant environment, like the previously reported actual CI results, than the classic ones, which implies that the new vocoding method could be a better alternative for acoustic modelling of current CI processing.
An Event-Driven ADR Approach for Residential Energy Resources in Microgrids With Uncertainties

In this paper, we present an event-driven automatic demand response (EADR) framework for online operation of residential microgrids (RMGs). Our framework involves comprehensive analysis of the schedulable ability (SA) for residential energy resources (RERs), and the uncertainties of both sides are also considered. Specifically, we first construct an EADR architecture to minimize the total operation cost and maintain supply–demand balance, and the event analyses are provided. Then, we define the concept of SA for RERs and also establish an SA evaluation system taking the real-time and historical information of the RERs into account. On these bases, SA-based interactions between RERs and EADR system and state machine are introduced to trigger the execution of the online EADR in a close-loop way. Furthermore, to implement this framework in practice, we functionally decompose the overall EADR architecture into event manager, EADR server, and a group of local controllers, and also design distributed online algorithms for each of them. The key idea of the designed algorithms is to operate the RMG simulating its real-life dynamics. Numerical simulations on a practical RMG verify the effectiveness of the proposed approach.
Automatic Analysis of Endoscopic Images for Polyps Detection and Segmentation

Polyps of the gastrointestinal tract are avowed as a risk factor for cancer. Early polyps detection and removal can reduce the probability of cancer. Quality of endoscopic examination strongly depends on the physician experience and other subjective factors. Automatic classification allows to increase effectiveness of gastroscopy. The present paper discusses the polyp segmentation task and its solution using automatic two-stage classification. The first stage is binary classification (presence / absence of polyp) based on the global features taken from the initial endoscopic images. The second stage involves the use of convolutional neural networks for segmentation. The result of the method is a pathology map with polyps marked by color. Evaluations on standard public databases show that the proposed method has specificity – 0.82 and sensitivity - 0.93.
Crosstalk-aware Routing, Modulation Level, Core and Spectrum Assignment, and Scheduling in SDM-based Elastic Optical Networks

The transmission capacity of traditional single core fibers is reaching its upper bound. Space-division multiplexing (SDM) leveraging multi-core fibers is considered to be a promising solution for this problem. Elastic optical networks (EONs) with Multi-core fibers (MCF) are a kind of SDM-enabled EONs (SDM-EON) providing larger transmission capacity. However, the resource assignment in these networks becomes more complex due to inter-core crosstalk. Another important issue to be considered in future networks is the time domain behavior of incoming traffic which means considering Advance Reservation (AR) requests, in addition to regular Immediate Reservation (IR) requests. In this paper, the problem of routing, modulation level, core and spectrum assignment and scheduling is addressed in an SDM-EON with AR/IR traffic, in which inter-core crosstalk is modeled. This problem is formulated as a Mixed Integer Linear Programming (MILP) and a heuristic algorithm is proposed to solve it.
Discovery of small molecules that bind to K-Ras and inhibit Sos-mediated activation.

K-Ras is a small GTPase that functions as a molecular switch cycling between inactive (GDP-bound) and active (GTP-bound) states. The conversion of K-Ras-GDP to K-Ras-GTP is the rate-limiting step in the activation of K-Ras and is catalyzed by guanine nucleotide exchange factors such as the son of sevenless (Sos). Mutations in K-Ras fix the protein in the active state and endow cells with capabilities that represent the hallmarks of cancer. [1] These include the ability to proliferate, evade apoptosis, reprogram cell metabolism, induce angiogenesis, activate invasion and metastasis, and escape immune destruction. [2] Indeed, aberrant K-Ras signaling plays a role in 30% of all human cancers, with the highest incidence of activating mutations found in pancreatic (70-90%), colon (30-50%), and lung (20-30%) carcinomas. [3] Downregulation of activated Ras reverses the transformed phenotype of cells and results in the dramatic regression of tumors in murine xenograft models. [4] Thus, K-Ras inhibition represents an attractive therapeutic strategy for many cancers. However, Ras activation and signaling is accomplished primarily through protein-protein interactions. Such protein interfaces typically lack well-defined binding pockets and have been difficult to target with small molecules. [5]
Adenylate cyclase toxin translocates across target cell membrane without forming a pore

The adenylate cyclase toxin‐haemolysin of Bordetella (CyaA) targets CD11b+ myeloid phagocytes and translocates across their cytoplasmic membrane an adenylate cyclase (AC) enzyme that catalyses conversion of cytosolic ATP into cAMP. In parallel, CyaA acts as a cytolysin forming cation‐selective pores, which permeabilize cell membrane and eventually provoke cell lysis. Using cytolytic activity, potassium efflux and patch‐clamp assays, we show that a combination of substitutions within the pore‐forming (E570Q) and acylation‐bearing domain (K860R) ablates selectively the cell‐permeabilizing activity of CyaA. At the same time, however, the capacity of such mutant CyaA to translocate the AC domain across cytoplasmic membrane into cytosol of macrophage cells and to elevate cellular cAMP concentrations remained intact. Moreover, the combination of E570Q+K860R substitutions suppressed the residual cytolytic activity of the enzymatically inactive CyaA/OVA/AC‐ toxoid on CD11b‐expressing monocytes, while leaving unaffected the capacity of the mutant toxoid to deliver in vitro a reporter CD8+ T cell epitope from ovalbumin (OVA) to the cytosolic pathway of dendritic cells for MHC class I‐restricted presentation and induce in vivo an OVA‐specific cytotoxic T cell response. CyaA, hence, employs a mechanism of AC enzyme domain translocation across cellular membrane that avoids passage across the cytolytic pore formed by toxin oligomers.
Combining Imitation Learning With Constraint-Based Task Specification and Control

This letter combines an combines an imitation learning approach with a model-based and constraint-based task specification and control methodology. Imitation learning provides an intuitive way for the end user to specify context of a new robot application without the need of traditional programming skills. On the other hand, constraint-based robot programming allows us to define complex tasks involving different kinds of sensor input. Combination of both enables adaptation of complex tasks to new environments and new objects with a small number of demonstrations. The proposed method uses a statistical uni-modal model to describe the demonstrations in terms of a number of weighted basis functions. This is then combined with model-based descriptions of other aspects of the task at hand. This approach was tested in a use case inspired by an industrial application, in which the required transfer motions were learned from a small number of demonstrations, and gradually improved by adding new demonstrations. Information on a collision-free path was introduced through a small number of demonstrations. The method showed a high level of composability with force and vision controlled tasks. The use case showed that the deployment of a complex constraint-based task with sensor interactions can be expedited using imitation learning.
WDLReconNet: Compressive Sensing Reconstruction With Deep Learning Over Wireless Fading Channels

Deep learning has been exploited in compressive sensing to reduce the computational complexity of reconstruction algorithms. However, existing deep-learning-based reconstruction algorithms might result in poor performance, when applied in wireless transmission environments. First, the impact of channel noise and fading on reconstruction has not been considered. Second, with the fully connected layers, most of these algorithms have been designed for a given sampling ratio, and thus cannot handle bandwidth variations. To tackle these problems, we propose a wireless deep learning reconstruction network (WDLReconNet). The most distinctive aspect of the WDLReconNet is that a feature enhancement layer (FEL) is designed and combined with the convolutional neural network (CNN). To combat the adverse impact from wireless transmission environments (i.e., channel noise and fading and bandwidth variations), FEL enhances features remained in compressive measurements by roughly recovering the signal based on dictionary learning. Then, this rough signal is processed by CNN to finalize reconstruction. In this way, the advantages of CNN in signal feature learning can be fully exploited for reconstruction. Furthermore, we propose fast-WDLReconNet to accelerate the training process of WDLReconNet. The experimental results demonstrate that the proposed algorithms outperform existing traditional and deep learning-based reconstruction algorithms under various scenarios.
Diversified Routing Queries in Dynamic Road Networks

Route planning and recommendation are increasingly important in the recent two decades. In this light, we propose and study novel diversified routing (DR) queries that discover the most convenient routes in dynamic road networks, where travel time, congestion probability, and global travel cost are taken into account. Given a dynamic transportation network, a source, a destination, a travel time threshold <inline-formula> <tex-math notation="LaTeX">$\tau _{t}$ </tex-math></inline-formula>, a congestion probability threshold <inline-formula> <tex-math notation="LaTeX">$\tau _{p}$ </tex-math></inline-formula>, and a global travel cost threshold <inline-formula> <tex-math notation="LaTeX">$\tau _{g}$ </tex-math></inline-formula>, the DR query finds: 1) the route with the minimum travel time whose global travel cost does not exceed <inline-formula> <tex-math notation="LaTeX">$\tau _{g}$ </tex-math></inline-formula>; 2) the route with the minimum congestion probability whose global travel cost does not exceed <inline-formula> <tex-math notation="LaTeX">$\tau.g$ </tex-math></inline-formula>; and 3) the route with the minimum global travel cost whose travel time does not exceed <inline-formula> <tex-math notation="LaTeX">$\tau _{t}$ </tex-math></inline-formula> and congestion probability does not exceed <inline-formula> <tex-math notation="LaTeX">$\tau _{p}$ </tex-math></inline-formula>. Such types of queries are very useful in many mobile applications, such as travel planning and recommendation, urban computing, intelligent transportation, and location-based services in general. The DR queries face two challenges: 1) how to model the dynamic road networks and to define the travel time, congestion probability, and global travel cost practically and 2) how to prune the search space effectively and to return the query results in the real time. To overcome the challenges and to process the DR queries efficiently, we develop a search framework based on network expansion. A series of optimization techniques is developed to further enhance the query efficiency. Finally, we conduct extensive experiments on large datasets to verify the performance of the developed algorithms.
Non-Volatile Memory File Systems: A Survey

For decades, computer architectures have treated memory and storage as separate entities. Nowadays, we watch the emergence of new memory technologies that promise to significantly change the landscape of memory systems. Byte-addressable non-volatile memory (NVM) technologies are expected to offer access latency close to that of dynamic random access memory and capacity suited for storage, resulting in storage-class memory. However, they also present some limitations, such as limited endurance and asymmetric read and write latency. Furthermore, adjusting the current hardware and software architectures to embrace these new memories in all their potential is proving to be a challenge in itself. In this paper, recent studies are analyzed to map the state-of-the-art of NVM file systems research. To achieve this goal, over 100 studies related to NVM systems were selected, analyzed, and categorized according to their topics and contributions. From the information extracted from these papers, we derive the main concerns and challenges currently being studied and discussed in the academia and industry, as well as the trends and solutions being proposed to address them.
Design of a Circularly Polarized Dielectric Resonator Antenna With Wide Bandwidth and Low Axial Ratio Values

A circularly polarized (CP) dielectric resonator antenna (DRA) with wide bandwidth and low axial ratio (AR) values is presented in this communication. The antenna consists of a stacked DRA, and it is fed by a substrate integrated waveguide coupling slot from the bottom. The stacked DRA structure, with high-permittivity dielectric strips layered on low-permittivity substrate, is adopted for bandwidth and gain benefits. Here, a pair of dielectric strips rather than a single one is placed above the coupling slot with 45° inclination for generating good CP radiation, and it is elaborately designed to provide three closely coupled CP modes, constituting a wide operating bandwidth. To facilitate the application in antenna array, a cavity-backed antenna design along with a $2 \times 2$ subarray are further presented. To verify the design, a subarray prototype is developed and tested at Ku-band for satellite applications, showing an impedance bandwidth of 25.1%, a wide 1 dB AR bandwidth of 15.6%, and a peak gain of 15.1 dBi.
Using Statistical Analysis to Fine-Tune the Results of Knapsack-Based Computational Platform Benchmarking

In previous papers, we composed an algorithmic foundation for computational platform benchmarking of well-known exact algorithms for the Knapsack Problem. We suggested using the run time of these algorithms with fixed inputs as the performance estimates. We then derived a single performance estimate, equally impacted by each of the algorithms. Although this approach makes for a reasonable general-purpose benchmark, equalizing the impact of different algorithms is not completely legitimate, as they have different processing requirements. In this paper, we perform an in-depth analysis of algorithm operational requirements and try to fine-tune the integral estimates to describe special-purpose (e.g. data compression or encipherment/decipherment) platforms more accurately.
Energy Consumption vs. Bit Rate Analysis Toward Massive MIMO Systems

In this paper, we provide a comprehensive study and comparison of the impact of both multiple-input multiple-output (MIMO) and massive MIMO techniques, at the base station (BS), on the energy consumption versus bit rate performance of a multi-antenna wireless handset. Both spatial diversity and spatial multiplexing (SM) are examined and compared for both known and unknown channel state information (CSI) at the transmitter. For the diversity case, our results show that for a BS with four or more receive antennas, there is little to be gained in terms of energy efficiency at the wireless handset by having more than one transmit antenna. For the SMcase with conventional MIMO, the bit rate increases linearly with Nt at the cost of additional energy consumption at the handset due to the absence of diversity gain. For massive MIMO, however, the excess receive antennas provide a diversity gain in a SM scheme thus allowing a high bit rate without sacrificing energy efficiency at the handset and even with no needs of CSI knowledge.