esa-sceva/satcom-chunk-collection huggingface dataset:
# Pro-Pre and Postbiotic in Celiac Disease
## 9 Pre-Biotics as Food Supplements in GFD  
The gluten-free diet exposes celiacs to nutritional deficiencies. In fact, the consumption of some nutrients, in particular fiber, iron, calcium and folic acid, is lower than normal in patients who follow a gluten-free diet. One possible strategy for avoiding the nutritional deficiencies in CD patients is appropriate dietary supplementation. According to the most recent definition, proposed by the International Scientific Association of Pro-biotics and Pre-biotics in 2017, pre-biotics are substrates that are used by the intestinal flora as an energy source; these substrates have a beneficial effect on human health [62]. Prebiotics can have beneficial effects on the absorption of vitamins and minerals; some of them have been shown to improve mineral bioavailability and increase iron absorption in animal studies [63].  
To date, all interventions with pre-biotics have been tested in celiac subjects at GFD with the aim of improving the subjects' nutritional status.  
In a randomized clinical trial with a placebo conducted on 34 patients with celiac disease, diagnosed according to the criteria created by the European Society of Gastroenterology, Hepatology and Pediatric Nutrition (ESPGHAN criteria 2012) the administration of 10 g per day of Sinergy 1 (oligortucose-enriched inulin, ORAFTI, Tienen, Belgium) resulted in a significant reduction of hepcidin (regulator of iron metabolism in the intervention group). No differences were found in the morphological and biochemical parameters of the blood (for details see Table 6) [64]. [PERSON] et al. observed that patients with GFD may counteract the reduction in _Bifidobacterium_ count in CD children after the administration of Synergy 1. The addition of Synergy 1 modified the faecal SCFA profile,and in particular, increased the concentration of acetates. In a second intervention study, the levels of 25-hydroxyvitamin D [25 (OH) D], parathyroid hormone, vitamins E and A, calcium, phosphate, magnesium, total protein and albumin were evaluated after administration of 10 g a day of Sinergy 1 for three months. The Synergy 1 leds to an increase in 25 (OH) D and vitamin E, but there was no difference in the levels of parathyroid hormone, calcium, phosphate and vitamin A [65, 66]. The circulating amino acid (AAs) concentrations are indicators of dietary protein intake and metabolic status. In CD, the AA imbalance is frequently observed. In two other papers, the integration of Sinergy 1 for three months led to an increase in urinary excretion of AA with a simultaneous increase in the content of circulating AAs, which could be attributed to higher absorption and/or intensified metabolism of AAs, as well as further healing of the intestinal mucosa [67]. Changes in Glu concentration suggest that oligofructose-enriched inulin could improve the intestinal condition and permeability, leading to benefits in bone metabolism, increasing bone formation rates and reducing bone resorption rates [68].
# Investigation of Fluid-Structure Interaction Induced Bending for Elastic Flaps in a Cross Flow
## 2 Material and Method
### Fluid equations  
The governing equations for time-dependent fluid regime are modeled by incompressible continuity and Navier-Stokes equations in terms of velocity \(v^{f}\), static pressure \(P\) and fluid density \(\rho^{f}\) as follows [38; 39; 40]:  
\[\mathrm{div}\;\dot{v}^{f}_{i}=\ 0\ \mathrm{in}\ \Omega_{\mathrm{f}}(\mathrm{t}) \tag{5}\]  
\[\rho^{f}\;\dot{v}^{f}_{i}=\sigma^{f}_{ij,j}\ \ \ \mathrm{in}\ \Omega_{\mathrm{f}}( \mathrm{t}). \tag{6}\]  
The fluid stress for an incompressible fluid is simplified to:  
\[\begin{array}{c}\sigma^{f}_{ij}=-P\delta_{ij}+\tau_{ij}\\ \tau_{ij}=\frac{2}{3}\mu\Big{(}\varepsilon_{ij}-\delta_{ij}\varepsilon_{kk} \Big{)}\\ e_{ij}=\ \left(v^{f}_{i,j}+v^{f}_{j,i}\right)\end{array} \tag{7}\]  
The dynamic mesh motion for an FSI problem is defined in terms of arbitrary movement of the grid points with respect to their frame of reference by considering the convection of the material points. The material derivative in terms of the movement of fluid mesh can be expressed as [40]:  
\[\frac{dv^{f}}{dt}=\ \frac{\partial v^{f}}{\partial t}+\left(v^{f}\ -U\right)\cdot \
abla v^{f}, \tag{8}\]  
where \(U\) is the mesh movement of fluid, also \(U\) = \(K\) _U_\({}_{sur}\) representing \(K\) as a stiffness matrix of an elastic media and _U_\({}_{sur}\) being the fluid dynamics surface mesh movement.  
Modeling of structure and fluid in ANSYS-Workbench are explained in the following sections:
## 2 Materials and Methods
### System Model
Alternatively, we divide the lower-level resolution nodes into groups, and as long as an index neighbor can be found in each group to satisfy Equation (1) for all users within that group, then Equation (1) can be satisfied for all users of the master node as well, as shown in Figure 3. In this situation, mapping records that need to be collaboratively stored for the master node will be assigned one copy to each index neighbor group for the purpose that these mapping records could be resolved by any user in different groups. Based on the above idea, the concept of index neighbor group is proposed as follows. \[indexNG^{k}(RN_{i})= \{indexNci^{k}\mid,[RN_{i+1}^{1},\ldots,RN_{i+1}^{n}]\mid latency (u_{i},RN_{i})+latency(RN_{i},indexNet^{k})+ \tag{2}\] \[latency(indexNci^{k},u_{i})<=2*T_{i},\] \[\forall u_{i}\in\{U(RN_{i+1}^{1})\cup\ldots\cup U(RN_{i+1}^{n})\} and\ u_{i}\in U_{i}\}\]  
In the above equation, an index neighbor group involves a master node, an index neighbor node, and a set of index neighbor group sub-nodes, where \(RN_{i}\) denotes the master node in level \(i\), \(indexNci^{k}(RN_{i})\) denotes the \(k\)-th index neighbor group of \(RN_{i}\), \(indexNci^{k}\) denotes the index neighbor node in \(indexNci^{k}(RN_{i})\), and \([RN_{i+1}^{1}\)\(\ldots\), \(RN_{i+1}^{n}]\) denotes the index neighbor group sub-nodes. The above inequality indicates that for any user of the sub-nodes, the corresponding index neighbor node in this group can provide a resolution service with deterministic service latency with the upper bound of \(T_{i}\), in the situation that this user sends a resolution request to the master node, and the request is then redirected to the index neighbor \(indexNet^{k}\), and the response is returned by the index neighbor finally. In Equation (2), \(latency(u_{i},RN_{i})\) denotes the latency from the user to the master node, which has an upper bound of \(T_{i}\) according to the hierarchical structure of LNMRS. \(Latency(RN_{i+1}^{k},u_{i})\)  
Figure 3: LNMRS model with two levels, with a master node and two index neighbor groups. denotes the latency from the index neighbor to the user, assuming that the user's corresponding resolution node in level \(i+1\) is \(RN_{i+1}^{t}\), the latency from \(RN_{i+1}^{t}\) to this user has an upper bound \(T_{i+1}\). The latency from \(RN_{i+1}^{k}\) to \(RN_{i+1}^{t}\) is measurable. It can be inferred that the upper bound of \(latency(RN_{i+1}^{k},u_{i})\) is \(latency(RN_{i+1}^{k},RN_{i+1}^{t})+T_{i+1}\). Since it is unpredictable which resolution node in level \(i+1\)\(u_{i}\) corresponds to, the upper bound of \(latency(RN_{i+1}^{k},u_{i})\) is \(max(latency(RN_{i+1}^{k},RN_{i+1}^{t}))+T_{i+1}\), where \(RN_{i+1}^{t}\) is any node in \(\{RN_{i+1}\}\). Therefore, the latency constraint in Equation (2) becomes  
\[latency(RN_{i},RN_{i+1}^{k})+max(latency(RN_{i+1}^{k},RN_{i+1}^{t}))<=T_{i}-T_{ i+1},\forall RN_{i+1}^{t}\in\{RN_{i+1}\}. \tag{3}\]
# Dynamic Performance of Soil-Tunnel System under Transverse Sinusoidal Excitations
## 5 Responses of Segmental Tunnel
### Dynamic Normal Earth Pressures of Model Tunnel  
Time histories of dynamic normal earth pressures on the external surface of the model tunnel are shown in Figure 22. Dynamic earth pressure in this paper is defined as an additional pressure caused by the earthquake. A positive value means that the total earth pressure increases compared with static earth pressure, and a negative value means that the total earth pressure decreases.  
Figure 20: The cross-sectional deformation of segmental tunnel. (**a**) Sin-2 (BD1/BD3). (**b**) Sin-2 (BD2/BD4). (**c**) Sin-3 (BD2/BD4). (**d**) Sin-6 (BD2/BD4).  
Figure 21: Extension/closure of longitudinal joints. (**a**) Sin-2. (**b**) Sin-6.  
Figure 22: The dynamic soil pressure of segmental tunnel. (**a**) Sin-2. (**b**) Sin-3.  
The dynamic normal earth pressures are relatively large in 45\({}^{\circ}\), 135\({}^{\circ}\), 225\({}^{\circ}\), and 315\({}^{\circ}\)direction of tunnel's cross-section, while reaches minimum at the top of tunnel. The earth pressure responses of symmetrical monitoring positions show almost 180 degrees out-of-phase. The distribution of dynamic earth pressure also reflects deformation characteristics of the tunnel. The deformations of the tunnel lead to changes in dynamic earth pressures around the tunnel, as concluded in [PERSON]'s paper [4], therefore, the greater deformation comes greater changes in dynamic earth pressures, as can be learned from Figure 20.  
In the cases of the Sin-2, Sin-3, and Sin-6, the response waveforms of dynamic normal earth pressures are irregular, and residual earth pressures exist at 45\({}^{\circ}\), 135\({}^{\circ}\), 225\({}^{\circ}\), and 315\({}^{\circ}\) directions of the tunnel's cross-section. In the cases of the Sin-9, Sin-11, Sin-14, and Sin-17, response waveforms of dynamic normal earth pressures are also irregular. The responses of dynamic earth pressures are relatively small, and the residual earth pressures can be neglected.  
These results reflect that the interactions between the soil and tunnel is much stronger when the excitation frequency approaches the fundamental frequency of the model ground. The settlement of the model ground occurred, indicating that the ground became denser during the shaking process and resulted in the residual earth pressures. In contrast, the interactions are rather weak under a higher frequency excitation.
## 2 Materials and Methods  
The primary purpose of this research is an in-depth comparison building on the brief initial comparison outlined in [23] to four sites covering a wide range of latitudes. From the equatorial region, Fortaleza, Brazil (FZA0M, 3.9\({}^{\circ}\)S, 321.6\({}^{\circ}\)E), was used over the time period October 2019-September 2021. From the mid-latitudinal regions, I-Cheon, South Korea (IC437, 37.1\({}^{\circ}\)N, 127.5\({}^{\circ}\)E), over the time period June 2010-April 2014, and Hermanus, South Africa (HE13N, 34.4\({}^{\circ}\)S, 19.2\({}^{\circ}\)E), over the time period July 2008-April 2014 were used. Gakona, Alaska (GA762, 62.4\({}^{\circ}\)N, 215.0\({}^{\circ}\)E) was used over the time period April 2007-April 2014 in the high latitudes. These dates were selected based on availability of GNSS-RO data in addition to the desire for low minimum frequencies, \(fmin\), measured by the ionosondes. By selecting sites from each latitude zone, a comparison of the GNSS-RO method performance in different regions was possible.
## 2 Materials and Methods
### Analysis and Amplifications of SSRs  
Broccoli and rapeseed were amplified using 93 pairs of SSR markers synthesized by the Beijing Genomics Institute (BGI, Beijing, China), with select broccoli inbred line 'B71' and rapeseed 'Huangshuang 5R' as the representative of diploid (CC) and tetrapolid (AACC), respectively, in the sprout period. Finally, 21 pairs of SSR markers which have clear bands and strong polymorphisms were selected to identify the collected broccoli germplasm materials containing more than 5 disease resistance loci.  
Ultimately, 21 SSRs were selected as shown in Table 2. The PCR amplification system is as follows: 10.0 \(\upmu\)L of Taq Master Polymerase (TaKaRa Bio, Dalian, China), 2.0 \(\upmu\)L of markers (1.0 \(\upmu\)L of P1 and 1.0 \(\upmu\)L of P2), 3.0 \(\upmu\)L of DNA template and 5.0 \(\upmu\)L of double-distilled water, total volume is 20.0 \(\upmu\)L. PCR amplification (BioRad, Hercules, CA, USA) was conducted with denaturing at 94 \({}^{\circ}\)C for 3 min, followed by 35 cycles of denaturation at 94 \({}^{\circ}\)C for 30 s, annealing at 55 \({}^{\circ}\)C for 30 s, extension at 72 \({}^{\circ}\)C for 45 s, and final synthesis at 72 \({}^{\circ}\)C for 7 min. The PCR products were separated on 8% nondenaturing polyacrylamide gels. After electrophoresis, the gels were stained as previously described [1; 16].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Accessions** & **Species** & **Generations** & **CR Genes** \\ \hline B910 & Broccoli (_Brassica oleraca_ L. var. _italica_) \(\times\) Turnip (_Brassica rapa_ L. ssp. _rapa_) & F\({}_{1}\) & CRa, CRb, Crr1, Crr2, Crr3 \\ B936 & Broccoli (_Brassica oleraca_ L. var. _italica_) \(\times\) Turnip (_Brassica rapa_ L. ssp. _rapa_) & BC1 & CRa, CRb, Crr1, Crr2, Crr3 \\ B1019 & Broccoli (_Brassica oleraca_ L. var. _italica_) \(\times\) & & \\ B1019 & Cabbage (_Brassica oleraca_ L. var. _capitalica_) \(\times\) & F\({}_{1}\) & CRa, CRb, Crr1, Crr2, Crr3 \\  & Turnip (_Brassica rapa_ L. ssp. _rapa_) & & \\  & Choi Sum (_Brassica campstris_ L. ssp. _chinensis_ var. _utilis Tsen et Le_) \(\times\) Chinese cabbage (_Brassica_ & F\({}_{1}\) & CRa, CRb, CRc, CRk, Crr1, Crr2, Crr3 \\  & _pekinensis Rupr._) & & \\ B1027 & Broccoli (_Brassica oleraca_ L. var. _italica_) \(\times\) Rape (_Brassica napus_ L.) & BC1 & CRa, CRb, Crr1, Crr2, Crr3 \\ \hline \hline \end{tabular} Note: Identification and details of CR loci in each cultivar are unpublished.  
\end{table}
Table 1: \(Cont.\)  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Marker Names** & **Forward Marker (5’ to 3’)** & **Reverse Marker (5’ to 3’)** \\ \hline LTSSR13 & TCATCCCCATACCTCTCCG & CCGGTTCAGTTGAAGAAA \\ LTSSR81 & GTCTGCCCATGTCTCTCTCTCTCTCT & TGGCATCACACTAGCTACGC \\ LTSSR94 & GCGAAACCAAAGAGAG & CAGAGGAAGGGGAAGAG \\ LTSSR132 & CCAAGAGAACCAGCAAGAG & AAATGTCAATGCCACTGCAA \\ LTSSR180 & TGAATATGATGGCGAGGGG & AACGGGGGGGAACGTTTAGATT \\ LTSSR188 & AGACAGCGGGAGAGACGGTATA & CGATAGAGAGAGAGGGGAACG \\ LTSSR304 & AAACAGAGACACCTTTGTGTCC & CTCTGTTTTCT
## 2 Mechanism of Strength Reduction Method  
The Strength Reduction Method (SRM) is an iterative procedure that continues until a certain condition is met, such as a failure criterion or a non-convergence criterion [4; 13]. For example, in a finite element analysis, the shear strength parameters (cohesion and angle of internal friction) of soil or rock slopes can be reduced until the slope \"fails\", i.e., the algorithm does not converge, and the deformations become severe. The SRM defines a safety factor (K) that quantifies how much the shear strength parameters (cohesion, C, and internal friction angle, \(\varphi\)) must be reduced to bring the slope to a critical failure state. To implement this method, start with the original slope parameters: cohesion, C\({}_{0}\), and friction angle, \(\varphi_{0}\). Reduce C and \(\varphi\) by a factor K (i.e., C = C\({}_{0}\)/K, \(\varphi\) = \(\varphi_{0}\)/K). Perform slope stability analysis using the reduced parameters. Progressively increase K and repeat analysis until slope reaches the critical failure state. When the slope reaches failure with K = K\({}^{\prime\prime}\), the corresponding C\({}^{\prime\prime}\) and \(\varphi^{\prime\prime}\) represent reduced shear strengths that result in a safety factor of 1. Compare K\({}^{\prime\prime}\) to the original parameters C\({}_{0}\) and \(\varphi_{0}\) to quantify the built-in safety factor of the original slope. Use this information to evaluate/improve slope design to achieve desired safety factors. The flowchart of the method is shown in Figure 2. Substituting the reduced shear strength parameters into the FOS equation gives Equation (1).  
\[\text{F}=\frac{\text{K}}{\text{K}^{\prime\prime}}=\text{K}=\frac{\text{C}_{0}} {\text{C}^{\prime\prime}}=\frac{\tan\mathcal{O}_{0}}{\tan\mathcal{O}^{\prime}} \tag{1}\]  
Figure 2: The flowchart of slope stability analysis using the Strength Reduction Method.
# Synthesis of Novel Chalcones as Acetylcholinesteraseerase Inhibitors
## 2 Materials and Methods
### In Vitro Inhibition Assay on AChE  
AChE-inhibitory activities of benzylaminochalcones derivatives **A1-A10** were determined by the [PERSON]'s method [26], using galantamine as a reference compound. Acetylcholinesteraseerase (AChE, E.C. 3.1.1.7, from electric eel), 5,5'-dithiobis-(2-nitrobenzoic acid) (DTNB), acetyl-thiocholine iodide (ATCI), galantamine were purchased from Sigma Aldrich (St. Louis, MO, USA). Tested compounds were dissolved in a minimum volume of 10% methanol in Tris buffer pH 8 to provide a final concentration range: 120 \(\upmu\)M; 60 \(\upmu\)M; 30 \(\upmu\)M; 15 \(\upmu\)M; 7.5 \(\upmu\)M.  
All the assays were performed in 96-well microtiter plates in the same condition for benzylaminochalcone and galantamine. Briefly, 30 \(\upmu\)L of 100 mM sodium phosphate buffer pH 8, 30 \(\upmu\)L of sample (several quantities of samples, all dissolved in methanol, in order to calculate IC\({}_{50}\) values) and 30 \(\upmu\)L acetylcholinesteraseoninesterase solution containing 0.54 U/mL were mixed in a well of 96 plate and allowed to incubate for 15 min at 25 \({}^{\circ}\)C. Subsequently, 30 \(\upmu\)L of a solution of ATCI (15 mM, dissolved in water) and 150 \(\upmu\)L of 3 mM Ellmen's reagent (DTNB) were added. The absorbance at 405 nm was read during the first 5 min of the reaction. A control reaction which was considered to have 100% activity was carried out using the same volume of methanol/water instead of tested solutions. All samples were assayed in triplicate.  
Figure 1: The synthesis of benzylaminochalcones.  
Percentage (%) of AChE inhibition is calculated based on the absorbance value as following:  
\[\%\text{ Inhibition}=\frac{(A_{0\text{E}}-A_{0})-(A_{1}-A_{0\text{t}})}{A_{0 \text{E}}-A_{0}}\times 100\% \tag{1}\]  
where \(A_{0\text{E}}\) is the absorbance value of the control blank sample plus enzyme; \(A_{0}\) is the absorbance value of the blank sample; \(A_{\text{t}}\) is the absorbance value of the test (chalcone) sample and \(A_{0\text{t}}\) is the absorbance value of the blank test sample. The compositions of sample solutions were prepared as follows (Table 1).  
Linear equation indicating the correlation between the common logarithm of the compound concentration (\(\mu\)M) and the percentage of AChE inhibition (%) was built, and from which the IC\({}_{50}\) values (concentration that inhibits 50% AChE activity) of studied benzylaminochalcones were extrapolated.
## 2 Materials and Methods
### Adaptive DBO for DPE
#### 2.3.3 Convergence Performance Test
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Type** & **NO.** & **Function** & **Min.** \\ \hline Unimodal & \(F_{1}\) & Shifted and Rotated Bent-Cigar Function & 100 \\  & \(F_{2}\) & Shifted and Rotated Schwefel Function & 1100 \\ Basic & \(F_{3}\) & Shifted and Rotated Lunacek bi-Rastrigin Function & 700 \\  & \(F_{4}\) & Expanded Rosenbrock plus Griewangk Function & 1900 \\  & \(F_{5}\) & Hybrid Function 1 (N = 3) & 1700 \\ Hybrid & \(F_{6}\) & Hybrid Function 2 (N = 4) & 1600 \\  & \(F_{7}\) & Hybrid Function 3 (N = 5) & 2100 \\  & \(F_{8}\) & Composition Function 1 (N = 3) & 2200 \\ Compose & \(F_{9}\) & Composition Function 2 (N = 4) & 2400 \\  & \(F_{10}\) & Composition Function 3 (N = 5) & 2500 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Characteristics of CEC2020 test functions 1. \begin{table}
\begin{tabular}{l l} \hline \hline
**Algorithms** & **Parameters** \\ \hline PSO (1995) [15] & \(\omega_{min}=0.2,\omega_{\max}=0.9;c_{1}=2;c_{2}=2\) \\ SSA (2017) [19] & \(PD=0.2;SD=0.1;R2=0.8\) \\ GWO (2014) [17] & \(a_{min}=0,a_{\max}=2\) \\ HHO (2019) [18] & \(\beta=1.5;r=0.5;E=0.5\) \\ PIO (2014) [14] & \(R=0.3;T_{2}=0.9;T_{1}=0.1\) \\ PO (2024) [20] & \(\gamma=1.5;\alpha=0.2\) \\ DBO (2023) [21] & \(k=\lambda=0.1;b=0.3;S=0.5\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters of swarm intelligence algorithms. However, it is worth noting that on function \(F_{1}\), SSA outperforms ADBO, and on function \(F_{9}\), PSO, SSA, and GWO were able to match or even surpass ADBO in performance. Despite these exceptions, ADBO outperformed the other algorithms in the remaining functions tested. In summary, the comparison results demonstrate ADBO's significant advantages in performance and its superiority across different types of problems, validating its overall effectiveness. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Fun. No. ** & & **PSO** & **SSA** & **GWO** & **HHO** & **PIO** & **PO** & **DBO** & **ADBO** \\ \hline \multirow{4}{*}{\(F_{1}\)} & Mean & \(3.70\times 10^{6}\) & \(5.74\times 10^{3}\) & \(2.73\times 10^{9}\)
# A Review of In-Situ and Remote Sensing Technologies to Monitor Water and Sanitation Interventions
## 2 Sensing WASH
### Microbial Sensors  
Akvo Caddisfly builds on the smartphone-based application Akvo Flow, a survey-based application similar to previously described smartphone and dashboard programs [28]. However, Akvo Caddisfly integrates pocket-sized water-testing devices directly to the smartphone, whereby results do not need to be entered by the user but are automatically measured and recorded. Currently, Akvo Caddisfly can measure several water-quality parameters. Fluoride is measured using a test chamber through which a reagent is added to the sample and a photo is taken to analyze the concentration based on the color generated by the sample and reagent mixture. Salinity is measured based on an electrical conductivity sensor attached directly to the phone via a USB cable. Additionally, any color-based test strip can be analyzed by Akvo Caddisfly by corresponding it to the color calibration card, thereby limiting the subjectivity of the test.  
A class of optical fluorimeters may be used as real-time in situ fecal contamination detectors. These sensors detect tryptophan-like fluorescence (TLF) associated with the presence of microbial contamination. In recent years, several laboratory-grade products have been validated in the field as predictive of _E. coli_[29; 30]. However, these instruments require frequent cleaning, are not intended for long-term autonomous operation, and cost $7500 or more.  
These state-of-the-art fluorimeter solutions are not designed for in situ long-term use. This is primarily because biofouling and baseline drift attenuate and confuse the signal [31]. At present, there are no viable in-situ electronic sensors for monitoring the microbial contamination of drinking water. The closest products feasible for this application are spectroscopy-based sensors to detect TLF from _E. coli_. The United Nations Children's Fund (UNICEF) has recently published a Target Product Profile detailing a market need for improved sensor systems for the microbial contamination of drinking water [32].