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    Thyroid peroxidase (TPO) is essential for the synthesis of thyroid hormones. However, specific mutations render TPO antigenic and prone to autoimmune attacks leading to thyroid cancer, TPO deficiency, and congenital hypothyroidism (CH). Despite technological advancement, most experimental procedures cannot quickly identify the genetic causes of CH nor detect thyroid cancer in the early stages.


    We performed saturated computational mutagenesis to calculate the folding energy changes (∆∆G) caused by missense mutations and analyzed the mutations involved in post-translational modifications (PTMs).


    Our results showed that the functional important missense mutations occurred in the heme peroxidase domain. Through computational saturation mutagenesis, we identified the TPO mutations in G393 and G348 affecting protein stability and PTMs. Our folding energy calculations revealed that seven of nine somatic thyroid cancer mutations destabilized TPO.


    These findings highlight the impact of these specific mutations on TPO stability, linking them to thyroid cancer and other genetic thyroid-related disorders. Our results show that computational mutagenesis of proteins provides a quick insight into rare mutations causing Mendelian disorders and cancers in humans.

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    Free, publicly-accessible full text available March 1, 2025
  2. Abstract

    The [Cii] 158μm emission line and the underlying far-infrared (FIR) dust continuum are important tracers for studying star formation and kinematic properties of early galaxies. We present a survey of the [Cii] emission lines and FIR continua of 31 luminous quasars atz> 6.5 using the Atacama Large Millimeter Array (ALMA) and the NOrthern Extended Millimeter Array at sub-arcsec resolution. This survey more than doubles the number of quasars with [Cii] and FIR observations at these redshifts and enables statistical studies of quasar host galaxies deep into the epoch of reionization. We detect [Cii] emission in 27 quasar hosts with a luminosity range ofL[CII]= (0.3–5.5) × 109Land detect the FIR continuum of 28 quasar hosts with a luminosity range ofLFIR= (0.5–13.0) × 1012L. BothL[CII]andLFIRare correlated (ρ≃ 0.4) with the quasar bolometric luminosity, albeit with substantial scatter. The quasar hosts detected by ALMA are clearly resolved with a median diameter of ∼5 kpc. About 40% of the quasar host galaxies show a velocity gradient in [Cii] emission, while the rest show either dispersion-dominated or disturbed kinematics. Basic estimates of the dynamical masses of the rotation-dominated host galaxies yieldMdyn= (0.1–7.5) × 1011M. Considering our findings alongside those of literature studies, we found that the ratio betweenMBHandMdynis about 10 times higher than that of localMBHMdynrelation on average but with substantial scatter (the ratio difference ranging from ∼0.6 to 60) and large uncertainties.

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  3. Abstract

    Feroxyhite (δ-FeOOH) nanomaterials were successfully synthesized through the atmospheric AC microplasma method at room temperature from ferrous sulfate aqueous solutions. Various syntheses conditions, including electric voltage, electric field strength, ferrous concentration, hydrogen peroxide concentration, and reaction duration, were systematically investigated. The synthesized products were characterized through x-ray diffraction, UV–vis absorption spectroscopy, photoluminescence spectroscopy, infra-red spectroscopy, and electron microscopy. The bandgap of the produced materials were strongly dependent of the ferrous concentration while the product ratio was dependent on all experimental conditions. The synthesis mechanism was thoroughly discussed. The synthesized nanomaterials were amorphous nanospheres, showing superparamagnetic properties at room temperature. The synthesized oxyhydroxide is a potential photovoltaic material besides its reported applications in photocatalysts and supercapacitors. The application of this synthesis technique could be extended to synthesize other oxy-hydroxide nanomaterials for renewable energy applications facilely, scalablely, cost-effectively, and environmentally.

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    Free, publicly-accessible full text available December 4, 2024
  4. ABSTRACT The compaction measurements of Quaternary and Tertiary Gulf Coast aquifer system sediments in the Houston-Galveston region (TX) show spatially variable compression of 0.08 to 8.49 mm/yr because of geohistorical overburden pressure when groundwater levels in the aquifer system were stable after about the year 2000. An aquifer-system creep equation is developed for evaluating this variable compression, with a thickness-weighted average creep coefficient based on Taylor's (1942) secondary consolidation theory. The temporal variation of aquifer system creep can be neglected in a short-term observation period (such as a decade) after a long-term creep period (such as over 1,000 years) in geohistory. The creep coefficient of the Gulf Coast aquifer system is found to be in a range of 8.74 × 10−5 to 3.94 × 10−3 (dimensionless), with an average of 1.38 × 10−3. Moreover, for silty clay or clay-dominant aquitards in the Gulf Coast aquifer system the creep coefficient value varies in the range of 2.21 × 10−4 to 3.94 × 10−3, which is consistent with values found by Mesri (1973) for most soils, which vary in the range of creep coefficient, 1 × 10−4 to 5 × 10−3. Land subsidence due to secondary consolidation of the Gulf Coast aquifer system is estimated to be 0.04 to 4.33 m in the 20th century and is projected to be 0.01 to 0.64 m in the 21st century at the 13 borehole extensometer locations in the Houston-Galveston region. The significant creep should be considered in the relative sea level rise, in addition to tectonic subsidence and primary consolidation. 
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  5. Existing adversarial algorithms for Deep Reinforcement Learning (DRL) have largely focused on identifying an optimal time to attack a DRL agent. However, little work has been explored in injecting efficient adversarial perturbations in DRL environments. We propose a suite of novel DRL adversarial attacks, called ACADIA, representing AttaCks Against Deep reInforcement leArning. ACADIA provides a set of efficient and robust perturbation-based adversarial attacks to disturb the DRL agent's decision-making based on novel combinations of techniques utilizing momentum, ADAM optimizer (i.e., Root Mean Square Propagation, or RMSProp), and initial randomization. These kinds of DRL attacks with novel integration of such techniques have not been studied in the existing Deep Neural Networks (DNNs) and DRL research. We consider two well-known DRL algorithms, Deep-Q Learning Network (DQN) and Proximal Policy Optimization (PPO), under Atari games and MuJoCo where both targeted and non-targeted attacks are considered with or without the state-of-the-art defenses in DRL (i.e., RADIAL and ATLA). Our results demonstrate that the proposed ACADIA outperforms existing gradient-based counterparts under a wide range of experimental settings. ACADIA is nine times faster than the state-of-the-art Carlini & Wagner (CW) method with better performance under defenses of DRL. 
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