Cystic Fibrosis: Improving quality of life

Cystic Fibrosis (CF) or Mucoviscidosis is an inherited condition. In cystic fibrosis transmembrane conductance regulator (CFTR) protein does not functions properly i.e regulation of fluids and salts outside the cells. Cystic fibrosis affects exocrine glands eg., the mucus-secreting and sweat glands in the respiratory and digestive systems. The frequency of common mutation F508del (deletion of phenylalanine residue at position 508) in children is between 19% and 34%. The estimate frequency of CF as 1:10,000 to 1:40,000 in children. There is no cure for cystic fibrosis, but treatment can reduce symptoms and complications to improve quality of life. Close monitoring and early, aggressive intervention is recommended to slow the progression of CF, which can lead to possible longer life.

Looking at Quantization of a Wave Function, From Weber (1961), to Signals From Wavefunctions at the Mouth of a Wormhole

We utilize how Weber in 1961 initiated the process of quantization of early universe fields to the problem of what may be emitted at the mouth of a wormhole. While the wormhole models are well developed, there is as of yet no consensus as to how, say GW or other signals from a wormhole mouth could be quantized, or made to be in adherence to a procedure Weber cribbed from Feynman, in 1961. In addition, we utilize an approximation for the Hubble parameter parameterized from Temperature using Sarkar’s H ~ Temperature relations, as given in the text . Finally after doing this we go to the Energy as E also ~ Temperature, and from there use E (energy) as ~ signal frequency. This gives us an idea of how to estimate frequency generated at the mouth of a wormhole.

Deep Learning versus Spectral Techniques for Frequency Estimation of Single Tones: Reduced Complexity for Software-Defined Radio and IoT Sensor Communications

Despite the increasing role of machine learning in various fields, very few works considered artificial intelligence for frequency estimation (FE). This work presents comprehensive analysis of a deep-learning (DL) approach for frequency estimation of single tones. A DL network with two layers having a few nodes can estimate frequency more accurately than well-known classical techniques can. While filling the gap in the existing literature, the study is comprehensive, analyzing errors under different signal-to-noise ratios (SNRs), numbers of nodes, and numbers of input samples under missing SNR information. DL-based FE is not significantly affected by SNR bias or number of nodes. A DL-based approach can properly work using a minimal number of input nodes N at which classical methods fail. DL could use as few as two layers while having two or three nodes for each, with the complexity of O{N} compared with discrete Fourier transform (DFT)-based FE with O{Nlog2 (N)} complexity. Furthermore, less N is required for DL. Therefore, DL can significantly reduce FE complexity, memory cost, and power consumption, which is attractive for resource-limited systems such as some Internet of Things (IoT) sensor applications. Reduced complexity also opens the door for hardware-efficient implementation using short-word-length (SWL) or time-efficient software-defined radio (SDR) communications.

A Blind Parameter Estimation Method of Frequency Hopping Signal with Low SNR

In order to effectively estimate the parameters of the frequency hopping signals under low signal-to-noise ratio (SNR), a blind parameter estimation method based on the modified discrete time Wigner-Ville distribution (MDTWVD) is proposed. We choose a low order Chebyshev polynomial as the kernel function for reducing the cross-term. Then, the parameters of the frequency hopping signals are finally obtained from the MDTWVD. The simulation experiment results show that the method used in this paper can effectively and accurately estimate frequency hopping signals parameters, especially under low SNR condition compared with other estimating methods.

Bone-conduction threshold and air-bone gap may predict frequency-specific air-conduction threshold after tympanoplasty

Postoperative hearing improvement is one of the main expectations for patients receiving tympanoplasty. The capacity to predict postoperative hearing may help to counsel a patient properly and avoid untoward expectations. It is difficult to predict postoperative hearing without knowing the disease process in the middle ear, which can only be assessed intraoperatively. However, the duration and extent of the underlying pathologies may represent in bone-conduction threshold and air-bone gap. Here in patients undergoing tympanoplasty without ossiculoplasty, we sorted and separated the surgery dates into the first group to build the predicting models and the second group to test the predictions. There were 87 and 30 ears, respectively. No specific enrollment or exclusion criteria were based on underlying pathologies such as the perforation size of the tympanic membrane or the middle ear conditions. The results show that bone-conduction threshold and air-bone gap together predicted air-conduction threshold after the surgery, including each frequency of 0.5k, 1k, 2k, and 4k Hz. The discrepancies between the predictions and recordings did not differ among these four frequencies. Of the variance in mean postoperative air-conduction threshold, 56.7% was linearly accounted for by these two preoperative predictors in this sample. The results suggest a trend that, the higher the frequency, the larger the part was accounted for by these two preoperative predictors. These together may help a surgeon to estimate frequency-specific hearing outcome after the surgery, answer patients’ questions with quantitative statistics, and counsel patients with proper expectations.

Deep Learning versus Spectral Techniques for Frequency Estimation of Single-Tones: Reduced Complexity for SDR and IoT Sensor Communications

Despite the increasing role of machine learning in various fields, very few works considered artificial intelligence for frequency estimation (FE). This work presents a comprehensive analysis of deep-learning (DL) approach for frequency estimation of single-tones. It is shown that DL network with two layers having a few nodes can estimate frequency more accurately than well-known classical techniques. The study is comprehensive, filling gaps of existing works, where it analyzes error under different signal-to-noise ratios, numbers of nodes, and numbers of input samples; also, under missing SNR information. It is found that DL-based FE is not significantly affected by SNR bias or number of nodes. DL-based approach can work properly using minimal number of input nodes N at which classical methods fail. It is possible for DL to use as little as two layers with two or three nodes each, with complexity of O{N} versus O{Nlog2 (N)} for DFT-based FE, noting that less N is required for DL. Hence, DL can significantly reduce FE complexity, memory, cost, and power consumption, making DL-based FE attractive for resource-limited systems like some IoT sensor applications. Also, reduced complexity opens the door for hardware-efficient implementation using short word-length (SWL) or time-efficient software-defined radio (SDR) communications.

Analysis of Seismic Signals Generated by Vehicle Traffic with Application to Derivation of Subsurface Q-Values

Correct identification and modeling of anthropogenic sources of ground motion are of considerable importance for many studies, including detection of small earthquakes and imaging seismic properties below the surface. To understand signals generated by common vehicle traffic, we use seismic data recorded by closely spaced geophones normal to roads at two sites on San Jacinto fault zone. To quantify the spatiotemporal and frequency variations of the recorded ground motions, we develop a simple analytical solution accounting for propagation and attenuation of surface waves. The model reproduces well-observed bell-shaped spectrograms of car signals recorded by geophones close to roads, and it can be used to estimate frequency-dependent Q-values of the subsurface materials. The data analysis indicates Q-values of 3–40, for frequencies up to 150 Hz for road-receiver paths at the two examined sites. The derived Q-values are consistent with attenuation factors of surface waves previously obtained with other methods. The analytical results and analysis procedure provide a highly efficient method for deriving Q-values of shallow subsurface materials.

Polymorphism of folate cycle genes as a risk factor of hyperhomocysteinemia

Hyperhomocysteinemia (HHc) is a new factor being considered at the moment that can cause damage to vessel walls. Its occurrence depends on genetic peculiarities of a body. Our research goal was to estimate frequency of genetic polymorphisms (SNP) in folate cycle genes among people living in Perm region and its influence on homocysteine (Hc) concentration in blood serum. We examined 189 women (32.2±5.25). Hc concentration in blood serum was determined with immune chemiluminescent procedure. We examined frequency of SNP in folate cycle genes with pyrosequencing. Homozygote state as per minor alleles in methylene tetrahydrofolate reductase (MTHFR) gene (rs 1801133 и rs 1801131) and MTR gene (rs 1805087) was registered 7.5, 5.4, and 13.75 times less frequently than homozygote state as per neutral alleles. Heterozygote state prevailed for genes of methionine synthase reductase and folate transport protein among examined SNP. Homozygotes as per minor allele SNP in MTHFR gene (Ala222Val; rs 1801133) had higher Hc concentration in blood serum that amounted to 8.476 ± 3.193 mmol/L and was 1.276 times higher than the same parameter in homozygotes as per neutral allele (р=0.0036). We didn’t establish any influence on Hc contents in blood serum for the remaining 4 SNP in folate cycle genes (р> 0.1). Examined SNP in MTHFR and MTR genes tended to have neutral alleles more frequently than minor ones. SNP in genes of other examined proteins belonging to folate cycle didn’t have any differences in frequency of examined alleles. We didn’t detect a combination of homozygote state as per two SNP in MTHFR gene or homozygote state as per one SNP and heterozygote state as per another one in a genome. Only SNP in MTHFR gene (Ala222Val, rs 1801133) authentically causes increase in homocysteine concentration out of all the examined SNP in genes of folate cycle enzymes and proteins.

Polymorphism of folate cycle genes as a risk factor of hyperhomocysteinemia

Hyperhomocysteinemia (HHc) is a new factor being considered at the moment that can cause damage to vessel walls. Its occurrence depends on genetic peculiarities of a body. Our research goal was to estimate frequency of genetic polymorphisms (SNP) in folate cycle genes among people living in Perm region and its influence on homocysteine (Hc) concentration in blood serum. We examined 189 women (32.2±5.25). Hc concentration in blood serum was determined with immune chemiluminescent procedure. We examined frequency of SNP in folate cycle genes with pyrosequencing. Homozygote state as per minor alleles in methylene tetrahydrofolate reductase (MTHFR) gene (rs 1801133 и rs 1801131) and MTR gene (rs 1805087) was registered 7.5, 5.4, and 13.75 times less frequently than homozygote state as per neutral alleles. Heterozygote state prevailed for genes of methionine synthase reductase and folate transport protein among examined SNP. Homozygotes as per minor allele SNP in MTHFR gene (Ala222Val; rs 1801133) had higher Hc concentration in blood serum that amounted to 8.476 ± 3.193 mmol/L and was 1.276 times higher than the same parameter in homozygotes as per neutral allele (р=0.0036). We didn’t establish any influence on Hc contents in blood serum for the remaining 4 SNP in folate cycle genes (р> 0.1). Examined SNP in MTHFR and MTR genes tended to have neutral alleles more frequently than minor ones. SNP in genes of other examined proteins belonging to folate cycle didn’t have any differences in frequency of examined alleles. We didn’t detect a combination of homozygote state as per two SNP in MTHFR gene or homozygote state as per one SNP and heterozygote state as per another one in a genome. Only SNP in MTHFR gene (Ala222Val, rs 1801133) authentically causes increase in homocysteine concentration out of all the examined SNP in genes of folate cycle enzymes and proteins