Using a conversion factor of 50, as applied by Hoppe et al. [29], the average phytoplankton carbon biomass

of 55 mg/m3 corresponds to a chl.a concentration of 1.1 mg/m3. This concentration meets the suggested target of 1.3 mg/m³ chl.a very well. TN and TP reference and target concentrations (annual near surface averages) for all German Baltic water bodies are documented in Appendix A1 and A2 and some results are summarized in Table 1. The existing ATR inhibitor target values for TN and TP for inner coastal waters (types B1 and B2) of Brockmann et al. [10] are in most cases and of Sagert et al. [42] for several water bodies unrealistic low because they do not take into account the individual situation of each water body. Both approaches suffer from

several weaknesses. (a) the riverine loads in Brockmann et al. [10] calculated with MONERIS did not reflect a real historic situation but assume artificial background concentrations and loads; (b) the natural gradients of nutrient concentration between river and open sea and especially the role of inner coastal waters as retention and transformation units for nutrients calculated by Brockmann et al. [10] are neglected; (c) hydrodynamic processes and spatial transport in the Baltic sea as well as the exposition MK-2206 supplier of water bodies towards pollution sources are neglected and finally, (d) explicit assumptions concerning the nutrient loads from neighboring states and other Baltic regions are lacking. For Bornholm Basin, Arkona Basin and Danish Straits, Carstensen et al. [14] suggest chl.a target concentrations of 2.44; 1.89 and 1.44 mg/m³ chl.a. Spatially integrating our results over the surface area of these Baltic Sea basins, we receive similar concentrations of 1.97 (Bornholm Basin), 1.79 (Arkona Basin) and 1.56 mg/m³ chl.a (Danish straits). Therefore, the proposed target values for the western Baltic Sea by Carstensen et al. [14] are largely confirmed (Table 1, Fig. 7). The small difference can Edoxaban be largely explained by

the different approaches and differences in the considered period for the analysis. Not for all water body types the calculation of DIN and DIP winter reference and target concentrations the methodology described above (multiplication of a factor with present data) provided convincing results, when compared to data (Fig. 9). This is especially true for inner coastal waters (types B1 and B2). As an alternative, DIN and DIP winter target concentrations were calculated based on average annual TN resp. TP concentrations. For every water body sub-type a separate linear regression between winter DIN (DIP) and average annual TN (TP) was established with the following coefficients of determination (R²) for the sub-water body types: B1 0.28; B2a 0.35; B2b 0.74; B3a 0.39; B3b 0.73; B4 0.59. In outer coastal waters and the open sea both methods show comparable results.

While a simple linear relationship between inflow and (SWWA) rainfall is sufficient to describe much of the variability in observed inflows, the most recent data confirms that the relationship appears to have changed after 1976, with less inflow for a given rainfall amount. The role of temperature in this changed relationship has been investigated but we find that any apparent correlations reflect the fact that rainfall and temperature tend to be inversely related and that temperature and inflow data exhibit long-term variability. When these factors are accounted for there

is no evidence that local temperature changes have any direct effect on inflows. This suggests that other explanations for the changed relationship between rainfall and inflows are more likely. For ABT263 Z-VAD-FMK in vitro example, the combined effects of changes in timing of rainfall events throughout the year, the absence of very heavy rainfall events and long-term changes in the physical character of the catchments – most likely changes to ground water levels. As was found in analyses of previous climate model experiments, the latest set of climate model results (CMIP5, RCP8.5) all project a decline in annual rainfall by the end of the century accompanied

by relatively large uncertainty. Some models (ACCESS1-3, BNU-ESM, CMCC-CESM, IPSL-CM5B-MR, IPSL-CM5B-LR, MPI-ESM-LR and NORESM1-M) exhibit time series that exhibit similarities to the observed SWWA time series in terms of a late 20th century decline. This confirms early interpretations that suggested that both natural variability and the enhanced greenhouse effect have contributed to the rainfall decrease. The climate change projections 17-DMAG (Alvespimycin) HCl continue to indicate a pessimistic outlook for rainfall – a finding consistent with those presented in previously published studies. Despite the consensus amongst the models, there

is still a relatively wide range in the magnitude of the projected decline by the end of the century. Given this range, plus the fact that we have only considered the results associated with a single emissions scenario, we have made no attempt to deal with this uncertainty. The fact that the CMIP5 projections do not differ substantially from previous model projections suggests that further modeling experiments will not yield much more extra information. However, some climate-related questions still deserve attention. For example, are the projected rainfall decreases accompanied by similar changes to mean sea level pressure patterns and the frequency of rain-bearing systems? Is it possible to narrow the uncertainty in the projections by discriminating between models and/or downscaling the result? Otherwise it is apparent that changes in the rainfall/inflow relationship could be just as important, if not more so, than changes to rainfall.

, 2011). In addition, 12% of the captured females in the York River of the Chesapeake Bay were egg-bearing (Havens et al., 2008). These results demonstrate that derelict traps could potentially impact the breeding population. In addition, in many cases dead organisms serve as bait that attracts other organisms (called “self-baiting”) to the DFT until the trap stops ghost fishing (Havens et al., 2008). Estimates suggest that self-baiting can double the catch rates of DFTs in some cases

(Havens et al., 2008). Derelict traps also impact non-target species. In North Carolina, for example, traps caught 45 taxa including shrimp, fish, urchins, and terrapins (Voss et al., 2012). In the USVI, during the six months that experimental fish traps were tracked, 42 species of fish from 21 families were trapped and one-fifth of the catch was non-target species (Clark et al., 2012). In Virginia, over 5000 fish (33 species) learn more were documented in DFTs including commercially important species such as Atlantic croaker Afatinib purchase (Micropogonias undulatas) and black sea bass (Centropristis striata) ( Bilkovic et al., 2014). DFTs may also catch threatened and endangered species, and species of concern. In North Carolina and Virginia, for example, diamondback terrapins are a concern because they encounter derelict traps in their preferred estuarine habitats ( Bilkovic et al., 2012 and Wood, 2010). Voss et al. (2012) found that at least 5 terrapins

were killed by DFTs in

their study area, with more suspected to have decomposed before observations were possible. Though the number of threatened and endangered species caught in DFTs may be relatively small, the loss of a few individuals can have significant population impacts because these species have small populations and are slow to reach reproductive maturity. Once traps stop ghost fishing, they may remain intact for long periods of time before degrading. See Fig. 3 for examples of how traps become fouled and begin to degrade in USVI, Maryland, and Alaska. In all three time series, traps maintain some structural integrity during the survey period (which ranges from several months in Maryland and USVI to over seven years in Alaska). These intact derelict traps can move along the seabed and negatively impact sensitive habitats. Surveys of fishing traps and Protirelin fishing-related gear in the Florida Keys determined that wind and severe weather events accumulated the highest density of fishing trap debris in the most sensitive habitats, corals; and research has shown that traps reduce eelgrass and salt marsh habitat by abrasion and smothering (Uhrin et al., 2005 and Uhrin and Schellinger, 2011). Research in Puget Sound noted a 30% improvement in eelgrass cover within one year of crab trap removal, and a similar study in coastal North Carolina found complete recovery of Spartina alterniflora (after a decline of 57.3% stem height and 67.

Over the past decade enterprising endoscopists mostly from Asia have extended Maraviroc the technique of ESD to enucleation of SETs. However, the concern with using ESD to enucleate muscularis propria (MP)-based SETs such as GISTs is that tumor residua may remain in the muscularis propria. Novel superior closure devices and the innovative submucosal tunnel technique which allows secure closure after transluminal interventions such as per oral endoscopic myotomy (POEM) have led to development of endoscopic full thickness resection techniques for

SETs. Direct transmural endoscopic full thickness resection (EFTR) and submucosal tunnel endoscopic resection (STER), an offshoot of POEM, have been reported by few groups in Asia over the past year. We present three initial cases of complete endoscopic removal of muscularis based SETs of the gastroesophageal (GE) junction and cardia using EFTR in two and STER in one patient. The three videos presented may represent the first reported EFTR and STER procedures for SETs in the United States. Complete resection

was achieved in all patients with short procedure times and no significant adverse events. These excellent outcomes are probably in large part due to our prior extensive experience with POEM, clip closure techniques, BIBF 1120 cell line and ESD for mucosal neoplasms as well as SETs. Unlike traditional ESD, EFTR and STER can achieve complete en bloc resection of MP-based SETs along with the associated MP thus ensuring R0 curative resection. These techniques represent a NOTES alternative to laparoscopic wedge resection. Advantages over laparoscopic

surgery include: 1. An incision-less approach and 2. Complete resection of SETs in areas that challenge laparoscopic resection such as the GE junction, esophagus and gastric cardia. “
“Gastric variceal hemorrhage (GVH) is a potentially life-threatening complication of portal hypertension. Cyanoacrylate injection achieves effective hemostasis in >90% of cases during Thiamine-diphosphate kinase GVH. However, TIPS preferred as first-line treatment for GV hemorrhage in many centers. Barriers to use of tissue adhesive include lack of familiarity with injection technique, concern for glue embolization and its off-label use. Its been shown that risk of glue related complications increases when larger volumes of glue are injected. The current method of probing the varix to assess consistency as a way to determine hemostasis is subjective. We describe the use of audible Doppler ultrasound (DopUS) signal as an objective means of gauging the volume of glue needed to achieve hemostasis. 64 y/o man with cirrhosis presented with hematemesis. EGD performed and source of GI hemorrhage found to be GV. DopUS used to guide glue injection. Hemostasis achieved. Patient with no recurrent GV hemorrhage at 6 months.

, 2000 and Vogt et al., 1998) whereas during actual task performance, small power (large event related desynchronization or ERD) is related to good performance (e.g., Doppelmayr et al., 2005 and Klimesch et al., 1997). Most interestingly for perceptual performance (in tasks target detection under threshold or near threshold conditions), small prestimulus alpha power (Ergenoglu et al., 2004) and a small ERD or even event related synchronization (ERS) during actual task performance (Hanslmayr et al., 2005) is predictive for good performance. A variety of studies have meanwhile documented

that a state Tanespimycin molecular weight of low prestimulus alpha power is associated with improved detection and discriminability of threshold-level stimuli (Hanslmayr et al., 2007a, Mathewson et al., 2009, Romei et al., 2007, Romei et al., 2008 and Van Dijk et al., 2008). There is, thus, good evidence for a double dissociation between pre- and poststimulus alpha power and the type of cognitive

PLX3397 cell line performance. Good memory performance is associated with large prestimulus but small poststimulus alpha power, whereas good perception performance is related to small prestimulus power with little or no ERD during perception performance. We have interpreted these findings in terms of cortical inhibition and excitation preceding task performance. Perception performance appears to be enhanced if the cortex already is activated (as indicated by small prestimulus power), whereas memory performance is enhanced if the cortex is

not activated (as indicated by large prestimulus power) before a task is performed. This interpretation is quite plausible if we assume Thalidomide that for visual target detection a high level of cortical excitation will be helpful to analyze a visual input. When a specified and well known target must be detected, memory traces are probably ‘preactivated’ and as a consequence inhibition must be reduced. For memory performance, on the other hand, an initial (prestimulus) activation of the cortex may be detrimental because it may interfere with (or even suppress) the high selectivity that is required for accessing a memory trace during actual task performance. In considering these findings and their interpretation, let us now make predictions for a traditional spatial cuing task in which a target must be detected in the right or left visual field. The prediction for prestimulus alpha power at the contralateral side is a decrease in power, whereas for the ipsilateral side, we expect an increase in power. Because the functional meaning of the P1 amplitude is similar to that of ongoing alpha, we also expect a larger ipsilateral P1. We have tested this prediction in Experiment 1 of the study by Freunberger et al. (2008a). As observed in other studies (e.g., Busch et al. 2004), we also found that the P1 is larger over ipsi- as compared to contralateral recording sites. In our study (using a type 2 paradigm with a jittered ISI between cue and target; cf. Freunberger et al.

Later, they incorporated the natural collagen and GAG network inhomogeneities into the model to calculate a more realistic set of parameters, and show that during CPA loading, cartilage undergoes a shrinking stress resulting from osmotic water loss from within the tissue toward the surrounding solution [4]. A recent

study by the same group, comparing spatially and temporally resolved measurements with Fick’s law and the new biomechanical model, showed that the CPA distribution can be significantly underestimated when using Fick’s law [3]. Such underestimation can result in longer than necessary CPA exposure of the chondrocytes within the matrix hence increasing the time-dependent toxicity of the CPA. During CPA loading to cartilage from a concentrated surrounding solution, there is an osmotic water selleck inhibitor flow to and from the cartilage when exposed to solutions of different osmolalities

which causes shrinking and swelling of articular cartilage during the CPA Y-27632 molecular weight loading (and removal) which was not accounted for in the cryobiology literature before the works of Abazari et al. [2] and [4]. In the context of biomechanical engineering, however, this water movement is known and included in the triphasic model of cartilage under mechanical or osmotic stress [39], [59] and [70]. Cartilage exhibits osmotic behavior similar to biological cells when exposed to different tonic environments: it swells and shrinks when exposed to hypo- and hypertonic solutions. The osmotic properties of cartilage are due to the presence of specific proteins within the cartilage matrix called proteoglycans. It is known that these osmotic properties contribute to the weight-bearing properties of articular cartilage by partially balancing the mechanical stress [69]. When cartilage is exposed to concentrated Urease CPA solutions, it shrinks and dehydrates due to osmotically-driven water movement from the

matrix to the solution. The extent of the shrinkage and the resultant stress–strain in the tissue matrix and effects on the chondrocytes may be important issues as described by Abazari et al. [4]. Also, after the diffusion of the CPA into the interstitial fluid, the tissue gains back the volume and swells. This shrink-swell behavior can be repeated a few times when cartilage is treated in a multistep loading protocol. In the biomechanical engineering literature, the adverse effect of cyclic mechanical stress and strain in the tissue matrix on the chondrocytes has been demonstrated [57]. Also, dehydration of cartilage and concentration of the salt ions in the interstitial fluid and the diffusion of the CPA into the cartilage change the osmotic environment of the chondrocytes [4]. The osmotic stresses on the chondrocytes under CPA loading protocol conditions have generally not been considered important in the field of cryobiology.

On the fourth week, the bone marrow cultures were recharged (fed as before, with 5 mL of growth medium containing a further 1 × 107 freshly isolated syngeneic femoral bone marrow cells from comparably aged mice as described by Gartner and Kaplan, 1980). Supernatants from LTBMC were harvested weekly from the 5th to 9th week of culture and frozen at −20 °C until required. The pooled cell suspensions were counted in a hemocytometer and centrifuged at 800g for 10 min, and the clonal growth of non-adherent progenitor cell populations was assayed weekly, as described in Section 2.4. The concentrations of IL-1α and IL-6 were evaluated in the supernatant of LTBMC. Cytokines

were quantified using a selleck compound sandwich ELISA (Enzyme-Linked ABT199 Immunosorbent Assay) in microtiter plates (96-well flat-bottom maxisorp microplate-NUNC, Roskilde, DM) using the following monoclonal antibodies purchased from R&D Systems: DuoSet® ELISA Development System Kit with purified anti-mouse IL-6 (Cat. DY406) and anti-mouse IL-1α/IL-1F1 (Cat. DY40). The cytokine levels were determined according to the R&D Systems cytokine ELISA protocol. Cytokine titers were expressed in pg per mL and were calculated by reference to standard curves constructed with known amounts of recombinant cytokines. For statistical analysis of changes in the progenitor cell assays, immunophenotyping, cytokine levels and colony-stimulating activity, analysis of variance (ANOVA

– two way) followed by the Bonferroni test was used to compare data among all groups. Statistical significance was reached when P < 0.05. The effects of CV treatment on the number of bone

marrow CFU-GM in animals subjected to SST or RST is demonstrated in Fig. 1A. The application of either SST or RST caused a significant PJ34 HCl reduction in CFU-GM (CTR: 18 ± 2 × 103, SST: 5 ± 1.5 × 103 and RST: 10 ± 1.5 × 103, P < 0.05). This reduction was higher in animals subjected to SST (SST: 5 ± 1.5 × 103 and RST: 10 ± 1.5 × 103, P < 0.05). The oral administration of 50 mg/kg of CV prevented the CFU-GM decrease in mice subjected to stressors, keeping CFU-GM numbers similar to control levels. CV treatment alone produced no changes in the number of CFU-GM in the bone marrow of normal mice. The effects of oral CV treatment were also evaluated on mature myeloid populations in animals subjected to both conditions (Fig. 1B). The percentage of Gr-1+Mac-1+ cells was reduced after SST and RST (CTR: 37 ± 3%, SST: 23 ± 1% and RST: 29 ± 2%, P < 0.05) with higher suppression after SST (23 ± 1%, P < 0.05). CV treatment prevented the changes induced by SST and RST on the Gr-1+Mac-1+ population, maintaining levels similar to those of the control group (CV + SST: 36 ± 2%, CV + RST: 41 ± 2% and CTR: 37 ± 3%). Representative histogram is demonstrated in Fig. 1C. The protective effects of CV oral treatment were also observed in B220+ (B lymphocyte) and CD3+ (T lymphocyte) lymphoid populations.

The main well-established effects of fenofibrate and fish oil on plasma lipids are their hypotriglyceridemic effects [4] and [20].

Indeed, we also found that both treatments similarly lowered serum triglyceride concentrations Gefitinib mouse and the number of large triglyceride–rich VLDL particles. These effects have been ascribed to an increased hepatic lipolysis and decreased lipogenesis [21] and [22], pathways which are under control of PPARα [2]. We demonstrated a small increase in HDL cholesterol concentrations after fenofibrate and fish oil treatment, reflected by increases in medium size and large size HDL particles. The increased delivery of surface remnants from the catabolism NU7441 datasheet of VLDL particles, together with a PPARα-induced expression of apoA1 and apoA2, the main apolipoproteins of HDL, may contribute to the raise in HDL cholesterol [23]. Furthermore, PPARα may stimulate reverse cholesterol transport via induction of ATP Binding Cassette protein A1 (ABCA1) [24]. Regarding the effects of fish oil and fenofibrate on triglycerides and HDL cholesterol, it is important to note that

the degree of these effects largely depend on baseline plasma lipid levels [4], [25] and [26]. In contrast to fenofibrate, fish oil increased LDL cholesterol concentrations. Others have also reported that high dose supplementation of EPA and DHA can raise LDL cholesterol by 5–10% [26]. In this respect, some groups of subjects may be more sensitive SB-3CT than other groups and it has been suggested that this variability in LDL cholesterol response is related to the apoE4 variant of apolipoprotein E [27]. For fenofibrate and fish oil treatments, it has been reported that the LDL particle size changes into a more buoyant type, which may be less atherogenic [5]. In our study, however, this could not be confirmed. Fish oil increased large, small and very small LDL compared to fenofibrate. These findings seem inconsistent in relation to our observed reduction in triglycerides and increase in large

HDL particles. When plasma triglycerides are reduced, the proportion or concentration of small LDL particles is expected to be reduced and that of large HDL increased [28]. We do not have an explanation for these unexpected results. Finally, we observed a non-significant increase of fasting plasma glucose after fish oil treatment. This agrees with a meta-analysis by Balk et al. [26], who reported a very small and non-significant average net increase in fasting plasma glucose after treatment with n-3 LCPUFAs. In summary, although n-3 LCPUFAs and fenofibrate can both activate PPARα, this study in overweight and obese subjects showed that both fenofibrate (200 mg/d) and fish oil (7.2 g/d, providing 1.7 g/d EPA and 1.2 g/d DHA) treatment for 6 weeks have different effects on cardiovascular risk markers.

After solving for K2T the term was converted to the free concentration scale from the total scale with equation(8) K2=K2T1+ST/KSwhere KS is the dissociation constant of HSO4− ( Dickson, 1990) and ST is the total sulfate concentration. Conversion from the free to total scale was necessary since Eq.  (7) is expressed on the free hydrogen ion concentration scale while − log(K2Te2) is expressed on the total scale. The H2I molar absorptivity terms in Eq.  (7) were determined in 1 M HCl, where the H2I form of the dye is dominant; the I2 − molar absorptivity terms were determined in solutions at pH = 12, where I2 − is dominant. To determine K1 values, AG-014699 mw an aqueous HCl–NaCl mixture (0.7 m

NaCl, pH ≈ 2) was prepared and CR absorbances were recorded after additions of standardized HCl at constant ionic strength. The pH in these experiments ranged from pH ≈ 2 to pH ≈ 1. Absorbances were corrected for dilution, and pH was calculated via HCl–NaCl mixing ratios. The absorbance maximum for the H2I form of the dye occurs at λ = 518 nm. Using 518A (measured) and [H+] (calculated), the following equation was fitted to obtain K1 as a function of temperature (282.40 ≤ T ≤ 307.91 K):

PD-166866 mouse equation(9) AλITs=εHI−λ+εH2IλH+/K11+H+/K1. Refined e1 estimates calculated via Eq.  (7) were subsequently used in Eq.  (2) to obtain refined estimates of − log(K2Te2) and K2. Iterative calculations using Eqs.  (2) and (7) were repeated until the − log(K2Te2) and e1 values stabilized to ± 10− 14 and ± 10− 9 respectively. Refinements of − log(K2Te2) through this process were extremely small;

the final − log(K2Te2) value was within 0.0001 of the initial estimate. Subsequent to the − log(K2Te2) and e1 determinations, SigmaPlot software was used to fit the pHmCP Fenbendazole and RCR data to Eq.  (10), thus producing an equation for calculation of seawater pHT from measurements of the CR absorbance ratio (RCR), sample temperature (T), and sample salinity (S): equation(10) pHT=a+bT+clnT−dT+logRCR−e11−RCRe3e2where − log(K2Te2) = a + b/T + c ln T − dT and the terms a, b, and c are functions of salinity. This equation is appropriate for pHT measurements made at atmospheric pressure for 278.15 ≤ T ≤ 308.15 K and 20 ≤ S ≤ 40. H2I, HI−, and I2 − cresol red absorbance maxima were observed to occur at 518 nm, 433 nm, and 573 nm, respectively (Fig. 1). These determinations of CR wavelengths for routine spectrophotometric pH measurements in seawater are consistent with those of Byrne and Breland (1989). Isosbestic point wavelengths as a function of temperature are well described with these equations, as shown in Fig. 2: equation(11) λisosH2I/HI=496.82−0.076T equation(12) λisosHI/I=513.01−0.092T. At 298.15 K, the H2I/HI− isosbestic point occurs at 474.2 nm and the HI−/I2 − isosbestic point occurs at 485.6 nm. The H2I/HI− isosbestic point wavelength decreases by 0.

SJ acknowledges support by the Cluster of Excellence and DFG Research Center Nanoscale Microscopy and Molecular Physiology of the Brain. “
“The Publisher regrets that the paper “Lessons on the critical interplay between

zinc binding and protein structure and dynamics” was supposed to have been identified as an “”Early Career Focused Review”" when it was published in the April 2013 issue (121C). Also, the cover art of the current issue (123C) is from the above mentioned article which was intended to appear in the cover of April 2013 issue (Volume 121C). The publisher would like to apologize for any inconvenience Selleck GDC0199 caused. “
“Recently, loads on propellers have been increasing due to the need for large and high-speed ships. Therefore, propeller cavitation is increasing, and the resulting adverse effects are becoming an important issue. Cavitation on a propeller induces pressure fluctuations on the hull. The limitation of tip clearance and an increase in higher order pressure fluctuation can cause severe ship vibration and a noise problem. Therefore, a technique allowing for the prediction and control of pressure fluctuations induced by propeller cavitation is needed at the design stage. The factors causing pressure fluctuation induced by a propeller are classified into three

primary parts: changes in the blade loading, rotation of the blade thickness, and the volume change of the propeller cavitation (Carlton, 2007). However, pressure fluctuation due to changes in blade loading and blade thickness are very small compared with the pressure fluctuations caused by cavitation. Various types of propeller cavitation, such as sheet cavitation, tip vortex cavitation, and bubble R428 in vivo cavitation, affect the hull pressure fluctuation. The peak pressure fluctuation

is measured in a discrete form at the blade rate frequency and is caused by unsteady sheet cavitation (Carlton, 2007). There have been numerous studies of the pressure fluctuation either caused by propeller cavitation (Kinns and Bloor, 2004, Merz et al., 2009, Lee et al., 1992, Cavitation Committee Report, 1987 and The Specialist Committee on Cavitation Induced Pressures, 2002). In recent years numerical prediction method using CFD is introduced and it shows good results (Pereira et al., 2004, Ji et al., 2011, Ji et al., 2012, Kehr and Kao, 2011, Luo et al., 2012 and Seo et al., 2008). Most studies investigated the correlation between predictions, model test results, and real ship measurements (Kim et al., 1996). Recently, the potential-based numerical prediction methods have been introduced that consider the physical propeller configuration and operating conditions. However, these numerical prediction methods make it difficult to intuitively understand the governing equation because they are presented in a form that is a result of solving potential-based boundary value problems. Moreover, these equations cannot represent the relative motion of the sources and the retarded time for the measurement point.