esa-sceva/satcom-chunk-collection huggingface dataset:
# Emerging Roles of Toxin-Antitoxin Modules in Bacterial Pathogenesis
## 7 Deciphering the Roles of TA Modules in Pathogenicity
### TAs and Virulence in Gram-Positive ESKAPE Pathogens  
The ESKAPE group of pathogens is responsible for a majority of hospital-acquired, multiresistant infections in the USA [198]. Among this group are _Staphylococcus aureus_ and _Enterococcus_ spp. which are Gram-positive, commensal bacteria that colonize the skin and respiratory tract, and the gastrointestinal tract, respectively. However, both species also can cause disease that ranges from transient infections to more serious, life-threatening illnesses. The recent emergence of antibiotic multiresistant strains, especially in hospital environments, has made treatment of staphylococcal and enterococcal infections much more problematic. Although TA loci are widespread in _S. aureus_[199], there are no definitive reports that link these TAs with pathogenicity. Nevertheless, TAs are implicated in staphylococcal persistence [200] and may regulate the production of important virulence factors in _S. aureus_[201; 202]. Moreover, the MazEF complex influences the sensitivity of _S. aureus_ to \(\beta\)-lactam antibiotics [203].  
TA genes are ubiquitous in enterococci, including on transferable resistance plasmids [27; 157; 204; 205]. Virulence plasmid pAD1 encodes a type I TA complex (_par_) in which translation of a 33 amino acid toxin specified by RNA I is blocked by a 66 nucleotide regulatory antitoxin, RNA II. Homologues of _par_ are widespread on bacterial genomes [206]. Deletion of the gene for a chromosomal RNA II in _Enterococcus faecalis_ enhanced virulence both in an insect infection model and in a mouse UTI model. The mutant strain also survived better than the wild-type strain in mouse macrophages, was more acid resistant, and showed improved growth when exposed to oxidative stress or bile salts [207]. The expression of RNA II antitoxin was downregulated in response to oxidative stress or bile salts suggesting that modulating expression of this chromosomal _par_ locus is key to survival under the hostile conditions encountered by _E. faecalis_ during infection. Moreover, the strain deleted of the RNA II gene showed global proteome changes, including elevated expression of an important transcription factor that is involved in virulence and the oxidative stress response [207]. These alterations correlate with the adaptation of _E. faecalis_ from the free-living to colonizing state. Despite its role in regulating translation of the cognate RNA I toxin gene, deletion of the RNA II antitoxin gene did not detectably boost expression of RNA I [207]. Thus, the molecular mechanisms that underpin the enhanced virulence of the _E. faecalis_ antitoxin mutant require further investigation. The potential roles of TA modules in the virulence capacity of other Gram-positive bacteria, such as _Streptococcus_ spp. [208] and _Clostridium_ spp. [209], which encompass a number of major human pathogens, largely is _tern incognita._
# Effectiveness of a Nutrition Education Program for Patients with Type 2 Diabetes Mellitus
## 2 Participants and Methods
### Lifestyle and Nutritional Intervention  
Each patient was allocated to one dietitian, a nutritionist, who was responsible for educating, measuring and adhering to the intervention. The intervention included one face-to-face meeting about nutrition under the direction of a dietician and the patient. During this visit, the patients were informed about the purpose of the study, answered the 24 h diet recall questionnaire [33] and were given an individualized nutritional plan (the energy requirements were calculated and personalized according to the preferences/needs of each patient) and detailed nutritional instructions, recommendations and advice, both for their diet and eating behavior. Patients were also educated regarding the choices of foods containing carbohydrates, with the aim of regulating sugar levels within normal limits as well as easily forming their daily diet. There were written specific nutritional recommendations: to have 5-6 meals per day, dividing the foods containing carbohydrates, to prefer foods rich in soluble fiber (legumes, fruits, vegetables, whole grains), to avoid the consumption of pure sugar and products containing it (sweets, cookies with sugar,cakes, jelly, ice creams with sugar, candies, sugared drinks, etc.), to reduce salt consumption and to avoid the consumption of saturated fat contained mainly in red meat, cold meats, egg yolks (up to 3 times/week) and butter. Emphasis was given to the consumption of vegetable fats, mainly olive oil, and to replace red meat with fish as much as possible. All participants' diets were evaluated to assess their compliance with the Mediterranean diet and standards recommended by the American Diabetes Association [34] and the Hellenic Diabetes Association [35]. Some dietary recommendations were given by the dietician, such as reducing the intake of calories, total fat to <30% of daily energy intake and saturated fat (including trans fatty acids) to <10% of daily energy intake and increasing fiber intake (15g to 30g/day) [36]. The patients were given a form with the food categories mentioned above and an individualized nutritional plan (55% carbohydrate, 15% protein, and 30% fat) that they could follow for weight loss, as this was a key target of the intervention for those who were overweight or obese. They also received instructions for exercise. More precisely, participants were instructed to follow a Mediterranean-type diet and to perform at least 150 min of moderate-intensity exercise a week. Adherence to recommendations from dietitians was reported after the three-month intervention. Finally, the patients could communicate, either through calling, messaging or visiting the dietician during the three-month intervention, for questions or for support for their efforts.
## 2 Related Works
### Kge  
Existing studies for KGE include TransE, TransH, TransR, and DistMult models [21]. [PERSON] et al. [7] proposed the TransE model. They treated the predicate in each triple as a mapping from subjects to objects and adjusted the corresponding vectors of head (subject), relation (predicate), and tail (object) to make \(h+r=t\). This model has few parameters, low computational complexity, good performance, and scalability on large-scale sparse data. However, the model only applies to a 1:1 relationship and cannot represent 1:N or N:1 relationship.  
To address this issue, the TransH model was proposed. The TransH model proposes that an entity has different representations in different relationships. The triangular closure relationship is broken by using a projection. The algorithm does not strictly require \(h+r=t\) but only needs to satisfy the projection collinear of head and tail nodes on the relational plane. In addition, tail nodes are replaced for N:1 relationship, and head nodes are replaced for 1:N relationship, satisfying \(h_{\perp}+r=t_{\perp}\).  
The abovementioned two models assume that entities and relationships are embedded in the same space. However, an entity may have multiple properties, hence there are cases where heads, tails, and relations are not in the same space. To solve this problem, the TransR model was designed. Its basic principle is to divide the whole space into entity and relation spaces. The head and tail nodes in the entity space are mapped to relation space via a relation matrix projection operation by projecting entities from entity space to the corresponding relation space and then building translations between projected entities. Figure 1 shows the entity and relation space of the TransE, TransH, and TransR models.  
The abovementioned three models can be collectively referred to as translation-based models. Moreover, bilinear-based models, such as RESCAL [22], DistMult, and complex [23], as well as neural network-based models, such as ConvE, have been investigated for deep learning.
# Time Series Feature Selection Method Based on Mutual Information
## 2 Methodology
### Construction of the Time Series Target Variable Based on PCA  
As mentioned before, for the feature selection problem of time series based on mutual information, one of the difficulties is finding appropriate target variables. In response to this issue, this paper proposes a time series target variable construction method based on PCA. By performing dimensionality reduction processing on multidimensional time series variables using PCA, variables that can characterize changes in system lifetime performance, which can be referred to as system operability, are obtained. On this basis, mutual information analysis is carried out between it and the monitoring variables of the system to conduct feature selection, so as to ensure that the selected features can maximize the prediction accuracy.  
Assuming that the dataset \(X=(x_{1},\,x_{2},\,\ldots,x_{L})\) contains \(L\) samples and \(x\) is an \(N\)-dimensional variable, then the empirical mean of \(x\) can be expressed as:  
\[u=(\mu_{1},\mu_{2},\ldots,\mu_{N})^{T} \tag{12}\]  
\[\mu_{n}=\frac{1}{L}\sum_{l=1}^{L}X[n,l] \tag{13}\]  
The covariance matrix of the samples can be expressed as:  
\[C=\frac{1}{L-1}(X-uh)\cdot(X-uh)^{T} \tag{14}\]  
where \(h\) is an \(N\)-dimensional vector that is all 1. The covariance matrix \(C\) is decomposed into eigenvectors, and the first \(M\) eigenvectors are selected. The selection of the number of eigenvectors \(M\) depends on the desired data variance. In this paper, in order to obtain a one-dimensional objective variable that can characterize the system operability, \(M=1\) is chosen.
# Design and Analysis of Joint Source-Channel Code System with Fixed-Length Code
## 2 Joint Source-Channel Code System
Then, \(N_{S}\) codewords are chosen from the set and assigned to the source symbols to minimize the cost function in Equation (9) while guaranteeing the codewords have the minimum Hamming distance, \(d_{H,\text{min}}\geq 2\), by using BSA. The algorithm for generating the source codebook is formulated as follows (Algorithm 1):  
```
Step 1 : Determine the code length \(L\) and crossover probability \(p_{c}\). Step 2 : Generate codewords set \(\mathbf{C}\ =\ \left\{\mathbf{c}^{(1)},\mathbf{c}^{(2)},\ldots\mathbf{c}^{(N_{S})}\right\}\), where the codeword \(\mathbf{c}^{(i)}\ =\ (c_{1}^{(i)},\ldots,c_{L}^{(i)})\) is the natural binary representations with index \(i\). Step 3 : Extend the number of source symbols from \(N_{S}\) to \(2^{L}\) by adding elements \(\left\{S_{N_{S}+1},S_{N_{S}+2},\ldots S_{2^{L}}\right\}\) with probabilities \(p_{k}\ =\ 0,N_{S}\ <\ k\ \leq\ 2^{L}\). Then, assign each symbol \(S_{i}\) to the codeword \(\mathbf{c}^{(i)}\) with the same index. Calculate the \(\cos\) t function \(D\) according to (9). Step 4: Rearrange the codewords to provide a minimum cost function by using the BSA: For \(p\ \leq 2^{L}\), \(p++\) For \(q\ =\ 1;\ q\ \leq 2^{L}\); \(q\
eq p\); \(q++\)  Try switching \(\mathbf{c}^{(q)}\) and \(\mathbf{c}^{(q)}\);  Calculate the new \(\cos\) t function \(\widetilde{D}\) according to (9) If \(\widetilde{D}>D\) then  switching \(\mathbf{c}^{(p)}\) and \(\mathbf{c}^{(q)}\); \(D\ =\ \widetilde{D}\) end if end for Step 5 : The first \(N_{S}\) rearranged codewords form the set \(\mathbf{C}\). Check the minimum Hamming distance of each codeword in \(\mathbf{C}\). If all the codewords have a minimum Hamming distance \(d_{H,\text{min}}\geq 2\), then output the set \(\mathbf{C}\) as the source code. If some codewords have a minimum Hamming distance \(d_{H,\text{min}}\ =\ 1\), remove them from the set \(\mathbf{C}\). Then, search the codeword of \(\mathbf{c}^{\prime}\) in order, and add the codeword that has a minimum Hamming distance, \(d_{H,\text{min}}\geq 2\), into \(\mathbf{C}\). ```  
**Algorithm 1** Binary Switch Algorithm (BSA)  
The source code can be obtained from the steps described above. It should be noted that the length of the codeword should be chosen to satisfy \(L\geq\log_{2}N_{S}+1\). For the block code with a fixed-length of \(L\), there are, at most, \(2^{L-1}\) possible codewords that satisfy the minimum distance, \(d_{H,\text{min}}\ =\ 2\). Therefore, to ensure the codewords are unambiguous, the number of possible codewords must satisfy \(2^{L-1}\geq N_{S}\), i.e., \(L\geq\log_{2}N_{S}+1\).
## 2 Materials and Methods
### Sensory Analysis  
This study was reviewed and approved by North Dakota State University IRB Federal Wide Assurance with the Department of Health and Human Services (FWA00002439). Informed consent was obtained from each subject prior to their participation in this study. The selected panelists represented research scholars, wine and cidermakers, and grape and apple growers who were familiar with wine and cider consumption and tasting. Prior to the cider evaluation, an introductory session was held to train the panelists, familiarizing individuals with the questionnaires and the meaning of each evaluated attribute. The tasting panelists included twelve men and three women, aged 35-60 years old, and the sensory evaluation of fermented 'Wodarz' cider was conducted in May 2021. Sensory evaluation and the number of panelists were limited due to sample volume; however, it was carried out as such due to the importance of the regionally developing interest in 'Wodarz' and the use of other dessert apples and yeast strains.  
In total, each taster evaluated fifteen coded samples (five yeast strains \(\times\) three field replicates) relating to smell and taste at room temperature (20-21 \({}^{\circ}\)C) using international tasting glasses (30 mL/sample). For accuracy, four sessions with three 10 min breaks were conducted during the total evaluation processes. During each session, 3 to 4 samples were evaluated by each taster. Based on a preliminary evaluation, a cider evaluation sheet was developed containing a cider aroma evaluation and a tasting evaluation that considered common aroma and taste classifications categories. The criteria for aroma evaluation included aroma intensity, fruity aroma, floral aroma, spice aroma, vegetal aroma, and yeast aroma. The tasting evaluation included acidity, floral taste, fruity flavor, spice taste, sweetness, and vegetal flavor. Each attribute was evaluated by means of a five-point scale from the lowest to the highest.  
The researchers developed a list of descriptors for taste and aroma based on a preliminary evaluation of common descriptors for wine and ciders; these descriptors were accumulated and transformed to check how they applied to the descriptor worksheets [30; 31; 32]. The panelists were provided with worksheets to check all of the descriptors that applied to specific cider samples. The aroma and taste descriptors included the following: aniseved, apple, apricot, banana, basil, black pepper, cherry, cinnamon, elderflower, fig, grape, grass, hay, herbal, honey, honeydew, kiwi, lemon, mint, mushroom, orange, peach, pear, pineapple, plum, prune, raisin, resin, rose, vanilla, watermelon, and yeast. While efforts to develop formal cider sensory lexicons, terminology, and evaluation criteria are ongoing, standardized methods are undergoing development [31; 32; 33; 34; 35].
## 3 Results and Discussion
### Validation Study  
In the conducted study, linearity, recovery, precision, limit of detection (LOD), limit of quantification (LOQ), and uncertainty (U) were assessed to validate the method. The validation was performed under optimized extraction and clean-up conditions using model wastewater. The linearity of the calibration curves was assessed using calibration solutions at concentrations of 0.005, 0.01, 0.05, 0.1, 0.5, and 1.0 \(\upmu\)g\(\cdot\)mL\({}^{-1}\) in solvent and wastewater extract on three consecutive days using LC-MS/MS. The evaluation results show that all compounds' coefficient of determination (R\({}^{2}\)) was 0.99 or higher. The recovery was determined in five replicates at three fortification levels: 0.001, 0.01, and 0.1 \(\upmu\)g\(\cdot\)mL\({}^{-1}\). All compounds showed satisfactory recoveries in the 71-97% range at 0.001 \(\upmu\)g\(\cdot\)mL\({}^{-1}\), 70-98% at 0.01 \(\upmu\)g\(\cdot\)mL\({}^{-1}\), and 73-99% at 0.1 \(\upmu\)g mL\({}^{-1}\). All pharmaceuticals yielded RSD not exceeding 16%. The RSD of twelve compounds at each level was less than 10%. The limit of quantification (LOQ) was determined as the lowest enrichment level confirmed by satisfactory recovery (70-120%) and RSD (\(\leq\)20%) values. The LOQ was in the range of 0.15-0.70 \(\upmu\)g\(\cdot\)mL\({}^{-1}\) for all compounds. The limit of detection (LOD) values of individual pharmaceuticals were calculated using the chromatogram when the signal-to-noise (S/N) ratio was higher than 3 [91]. The data obtained from the validation study were utilized to estimate the measurement uncertainty (U) associated with the analytical results. The expanded measurement uncertainties were determined using a \"top-down\" empirical model [92], with values ranging from 9% to 28% (coverage factor k = 2, confidence level 95%). Precision was identified as the primary source of uncertainty. Additionally, the uncertainty associated with recovery, calculated based on a rectangular distribution, was incorporated into the overall uncertainty budget to prevent an underestimation of the total uncertainty. The results are presented in Table 4, demonstrating the proposed method's suitability.
## Materials and Methods
### Identification of Faults, Malfunctions and Process Conditions
#### 2.3.1 Start-Up and First Testing Phase (2013-2014)
After this verification, a relationship was established between the quantitative parameters of the holes in the die and the compression rate as shown in Figure 10.  
Thus, the different parameters relate to each other in different ways, allowing a quantification of the compression rate. In this way, with the experience gained, it was possible to empirically define which die is suitable for the torrefied material. At the same time, this enabled the matrices to be replaced by adapting the compression rate, whatever the type, diameter or thickness, since it is sufficient to guarantee the desired value for compression rate, while varying or determining any of the other terms of the equation. Thus, the different lengths of the die hole are related to each other through the equation:  
\[\text{T}_{\text{I}}=\text{C}_{\text{I}}+\text{R}_{\text{I}}\]  
Figure 10: Scheme of the relationship between the constituent parameters between the holes in a pellet mill die, where C\({}_{\text{I}}\) is the compression length, R\({}_{\text{I}}\) is the relief length, T\({}_{\text{I}}\) is the total length and d is the diameter. where T1 is total length, C1 is compression length and R1 is relief length. This quantity is expressed in millimeters. Likewise, the diameter is related to the compression rate through the equation:  
\[\text{C}_{\text{R}}=\frac{\text{C}_{\text{I}}}{\text{d}}\]  
where C\({}_{\text{R}}\) is the compression rate and d is the diameter. This value is dimensionless. With these relationships, and after defining the ideal compression rate for the different degrees of torrefraction, it became possible to change, for example, the diameter of torrefied biomass pellets to be produced, or to verify whether a die, after rectification, is capable of producing torrefied biomass pellets with the desired quality. However, despite this development that allowed the production of high-quality torrefied biomass pellets, it was not yet possible to stabilize the production process in order to keep the material homogeneous, and there were many fluctuations in quality, mainly with regard to the degree of torrefraction. This difficulty in maintaining the homogeneity of the production process was essentially due to the fact that biomass is used as a source of thermal energy for the process. Or rather, it was due to the fact that biomass has a high thermal inertia and, therefore, does not allow rapid rises or falls in the temperature of the reactor to stabilize its temperature and, thus, the torrefaction process. This situation, together with the fact that the technology used is a rotary drum reactor with indirect heating, was immediately registered as disadvantageous for the selection of a torrefaction technology for an industrial-scale biomass torrefraction plant, since the greater the quantities of biomass processed, the greater and more difficult to control the problem will be.
## 4 Reconstruction of Wind Pressure Field on Roof Surface
### Power Spectrum Analysis  
The power spectrum represents the variation of signal power with frequency, that is, the distribution of signal power in the frequency domain. Based on the aforementioned analysis of energy ratio and statistical values, the aforementioned four feature points were still used for analysis, and the reconstruction results of the first 20 eigenmodes were selected to show the power spectrum situation. In the analysis of the first two sections, when the 20 eigenmode orders were selected for wind field reconstruction, the reconstruction based on mean wind pressure and fluctuating wind pressure both could achieve high-precision simulation of wind pressure field, and the error was extremely small. Therefore, only the reconstruction results based on the mean wind pressure are shown in this section, as shown in Figure 7.  
It can be seen from Figure 7 that the reconstructed wind pressure power spectral densities of the typical measuring points in the four zones are in good agreement with the test results. The power spectrum analysis results show that the effect is very good when the first 20 eigenmodes are used to reconstruct the wind pressure field.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**5 Eigenmode**} & \multicolumn{2}{c}{**10 Eigenmode**} & \multicolumn{2}{c}{**20 Eigenmode**} \\
**Measuring Point** & & \multicolumn{2}{c}{**Oders**} & \multicolumn{2}{c}{**Oders**} & \multicolumn{2}{c}{**Oders**} \\ \cline{3-8}  & & **Reconstructed Value** & & **Reconstructed Value** & & **Error** & **Reconstructed Value** & **Error** \\ \hline C1 & 808.5 & 789.7 & 2.3\% & 801.8 & 0.08\% & 808.5 & 0.0\% \\ E1 & 428.0 & 391.4 & 8.6\% & 417.6 & 2.4\% & 427.2 & 0.02\% \\ F1 & 276.5 & 265.3 & 4.0\% & 272.5 & 1.4\% & 276.5 & 0.007\% \\ K1 & 286.7 & 273.2 & 4.7\% & 280.5 & 2.1\% & 286.6 & 0.04\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: RMS value of wind pressure at each measuring point in the reconstructed wind field (based on fluctuating wind pressure).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**5 Eigenmode**} & \multicolumn{2}{c}{**10 Eigenmode**} & \multicolumn{2}{c}{**20 Eigenmode**} \\  & & \multicolumn{2}{c}{**Oders**} & \multicolumn{2}{c}{**Oders**} & \multicolumn{2}{c}{**Oders**} \\ \cline{3-8}  & & **Reconstructed Value** & & **Reconstructed Value** & & **Error** & **Reconstructed Value** & **Error** \\ \hline C1 & 808.5 & 789.7 & 2.3\% & 801.8 & 0.08\% & 808.5 & 0.0\% \\ E1 & 428.0 & 391.4 & 8.6\% & 417.6 & 2.4\% & 427.2 & 0.02\% \\ F1 & 276.5 & 265.3 & 4.0\% & 272.5 & 1.4\% & 276.5 & 0.007\% \\ K1 & 286.7 &