Nonetheless, not all pretreatments were satisfactory for simultaneous discrimination between roasted coffee,

roasted coffee husks and roasted corn. The spectra pretreatment steps that provided a satisfactory level of group separation when coffee and both adulterants were analyzed simultaneously were the following: (0) no additional treatment of raw data, (3) normalization with three point baseline correction and (4) first derivatives. The corresponding scatter plots obtained after Dasatinib concentration PCA analysis of the data (135 samples) are displayed in Fig. 4. Roasted coffee, roasted coffee husks and roasted corn can be identified as separated groups. Roasted corn is clearly separated from the others, whereas some group overlapping is observed between coffee and coffee husks for the spectra-based plots (Fig. 4a and

b). Complete separation of the three groups was obtained after submitting the spectra to first derivatives (Fig. 4c). Evaluation of the loadings plots obtained Seliciclib research buy after PCA analysis of raw spectra indicated that the spectral ranges that presented the highest influence on PC2 values in association with roasted corn were the following: 2250–1850 and 945–700 cm−1. In the wavenumber range 945–700 cm−1, the differences between the spectra are quite evident and they might be attributed to the presence of non-degraded starch in corn after roasting and its complete absence in roasted coffee and coffee husks (Amboni, Francisco, & Teixeira, 1999).

Differences can be also associated to the degree of saturation of the fatty acids in the triacylglycerol fraction of the coffee and corn oils (Guillén & Cabo, 1999), with the coffee oil presenting a higher degree of saturation than the corn oil (Moreau, 2002; Segall, Artz, Raslan, Jham, & Takahashi, 2005) and the correlated bands being displaced to higher wavenumbers (∼915 cm−1) than those for the corn oil. The highest influence on PC2 values in association with roasted coffee husks was observed in the range of 1600 to 1500 cm−1. In the case of normalized spectra, PtdIns(3,4)P2 the following ranges could be associated with separation of roasted coffee: 3040–3000, 2650–2350 and 1800–1760 cm−1. Loadings obtained for first derivatives could not be associated to specific regions in the spectra. The satisfactory group separation results obtained from the principal components analysis indicate that the data should provide enough information to develop classification models for roasted coffee and each specific roasted contaminant. Thus, linear discriminant analysis (LDA) was employed in order to obtain classification models (95% confidence). LDA models were constructed employing different numbers of variables, starting with all the data points and decreasing the number of variables.

To evaluate the impact of snowmelt runoff on nutrient pollution in the River Mukhavets, the total

amount of phosphate and ammonium ions during the winter period (December 2012–April 2013) was calculated for snowmelt runoff and river runoff in Brest (Table 3). The calculation was done for the overall mean concentrations of these pollutants in the Mukhavets Metformin solubility dmso for the last 3 years (Loginov 2012) and the overall mean concentrations in snowmelt runoff obtained in our study. The amounts of phosphate and ammonium ions discharged with snowmelt runoff make up 11.27% and 3.31% respectively of the total amount of these pollutants found in the Mukhavets during winter, showing that surface snowmelt runoff is a significant source of pollution by nitrogen and phosphorus compounds. If we take into account the fact that four towns with populations from 13 to 330 thousand people (Brest) are situated on the Mukhavets, the total pollutant load arising from surface snowmelt runoff from urban areas is even higher and presents a serious environmental threat at not only a regional but also a European scale. A potential threat arises from the fact that the River Mukhavets is a tributary of the Western Bug, a trans-boundary river of the Baltic Sea catchment area. As the mouth of the selleck chemicals Mukhavets is very close to the city, a significant

PTK6 percentage of the pollution released may be involved in trans-boundary transport, thereby contributing to the pollution and eutrophication of the

Baltic Sea. Unfortunately, we could not make similar calculations for the other pollutants because of the lack of appropriate river water monitoring data. The surface runoff formed during snow melting periods in Brest carries a significant pollutant load that exceeds national regulation levels and can cause long-term environmental effects on watercourses if the runoff is discharged into them without prior treatment. In Brest a significant percentage of the surface runoff is allowed to drain untreated into the River Mukhavets and flows with the river waters into the Western Bug, a trans-boundary river of the Baltic Sea catchment area. Thus, surface runoff from the Brest area can contribute to the trans-boundary transport of elements. The pollutants of primary concern during the winter period are TSS and chloride ions, because their concentrations show the greatest excess compared to MPCs, and phosphate and ammonium ions because of the eutrophication they may cause. “
“As defined in the EU Floods Directive (CEC 2007), the term ‘flood’ means ‘the temporary covering by water of land not normally covered by water’. The notion includes floods from rivers and mountain torrents, as well as floods from sea surges in coastal areas.

Our results

are in good agreement with data by Darecki & Stramski (2004) for the Baltic Sea, which showed poor agreement between in situ and satellite check details determinations of the normalised water-leaving radiance Lwn, especially in the blue spectral region (412–488 nm). The data for 551 nm showed the best agreement (unfortunately, the data for 531 nm was not included for lack of the corresponding spectral channel in the in situ spectroradiometer). The quality of the atmospheric correction in the Gulf of Finland was checked by Zibordi et al. (2009), but they presented the relative errors for the Lwn satellite retrieval, averaged over 100 matchups in different regions (Adriatic Sea, Atlantic Ocean, Persian Gulf) where only 20% were obtained in the Gulf of Finland. For our regional algorithm #8, formula (5) with data from Table 5 gives the following values of the ratio of Chlcalc/Chlmeas: range = 0.52–2.03, average = 1.16, standard Inhibitor Library clinical trial error = 0.50. Comparing them with the results of direct estimation given in Tables 1 and 3, one can see there is good agreement between both

estimates: the contribution to the errors in Chl retrieval from the atmospheric correction for this data subset makes up on average an overestimation of 16-17%. These estimates should be considered preliminary, since there were too few data to draw definitive conclusions. The main result of our work is a set of new regional Rucaparib algorithms for estimating chlorophyll (Chl) and suspended matter (TSM) concentrations in surface waters of the Gulf of Finland from MODIS satellite scanner data. The algorithms were developed on the

basis of data from field and satellite measurements in the study area in summers of 2012 and 2013 (40 stations); the data measured in situ included spectral values of the remote sensing reflectance Rrs, Chl and TSM concentrations. Testing of the existing algorithms with field data showed that all of them overestimated chlorophyll concentration several times, in particular, the standard MODIS algorithm (http://oceancolor.gsfc.nasa.gov/) overestimated Chl 4–19 times. The new regional algorithm for Chl estimation takes the form log Chl = –0.50 + 19.8X — 42.7X2, where X = log[Rrs(547)/Rrs(531)]; its validation with MODIS-Aqua data (10 stations) gave an average relative error of 20%. The bio-optical algorithm #8 contributes to this error ~ 3% ( Table 3) and the atmospheric correction – about 16-17% (see section 4.3). A new regional relationship between TSM and the particle backscattering coefficient bbp has been derived: log TSM = 0.79 log bbp + 1.95, where TSM is expressed in mg l−1 and bbp in m−1. It was calculated from the satellite data with using a previously developed algorithm (http://optics.ocean.ru). The coefficient of determination r2 for this regression equation is equal to 0.61, and the standard error is 0.6 mg l−1.

3, 30, 42, 43 and 44 For the PLX3397 ic50 specimens treated with the photopolymerized coatings, significant differences between smooth and rough surfaces were not detected. It has been reported that the more hydrophobic the surface, the greater is the C.

albicans cell adherence by area unit. 27 Thus, a commonly used method to reduce the attachment of microorganisms is surface modification with hydrophilic polymers 7, 21 and 24 as attempted in the present study. For instance, coating surfaces with a 2-methacryloyloxyethyl phosphorylcholine (MPC) co-polymer decreased both water contact angles and the adhesion of C. albicans. 6 Accordingly, Yoshijima et al. 28 also observed that hydrophilic coatings of denture acrylic surfaces reduced the adhesion of the hydrophobic C. albicans hyphae. More recently,

it has been also found that coating a denture base material with silica nanoparticles was effective in increasing surface hydrophilicity and decreasing C. albicans adherence. 29 Hence, in the present study, the surface free energy of the specimens was calculated. The total surface free energy is the sum of components arising from dispersive and polar contributions where the polar component describes the hydrophilic character and the dispersive component is associated with the hydrophobic character of the surface. While the dispersive component (or Lifshitz–van der Waals) is influenced by the particle size or specific surface area, the polar component is the result of different forces/interactions such as polar, hydrogen, inductive and acid–base CX-4945 datasheet interactions.45 Thus, while the dispersive component is affected by the surface roughness

(or specific surface area), the polar component is dependent on the surface activity, which is related to the surface functional groups such as hydroxyl, carbonyl, and carboxyl.45 Generally, in this study, the coatings application decreased the water contact ID-8 angle (data not shown) and increased the polar surface free energy component which may have arisen from a change in the surface polar group concentration in the coated specimens. Only minor significant differences were observed for the dispersive component. Therefore, although the dispersive (or non-polar) component of the surface free energy is numerically higher than the polar component, the polar component is the main factor in determining modifications of the total surface free energy. Thus, the values of the surface energy followed the same trend as the polar component. Compared to the control, mean surface free energy values of the rough surfaces coated with S30, S35 and HP30 were significantly higher which indicates increased wettability. These results were expected because it is known that the contact angles are decreased (more hydrophilic) by surface roughness for hydrophilic surfaces.46 The effect of saliva on the hydrophobicity of the surfaces was also evaluated.

5 ml/min onto a cation-exchange column (Mono S 5/50 GL) previously equilibrated with 0.02 M pH 5.6 Na-acetate buffer. The unbound proteins were

washed out with the same buffer and the bound protein fractions were eluted with a buffer which additionally contained 1 M NaCl using a non linear gradient from 0 to 100% NaCl. Fractions of 1 ml/tube were collected and the absorbance was monitored at 280 nm. Electrophoresis (Laemmli, 1970) was carried out at 25 mA and 100 V/gel in Tris–glycine buffer, pH 8.3, containing 0.01% SDS. Gels were stained with Coomassie Brilliant Blue R-250 or with silver nitrate. Protein concentrations were determined according to the microbiuret method (Itzhaki and Gill, 1964), using bovine serum albumin as the standard. The

learn more coagulant activity was performed qualitatively by evaluating the coagulation of human plasma in vitro. The minimum coagulant dose (MCD) was defined as the amount of enzyme able to clot plasma in 60 s ( Theakston and Reid, 1983). The assay was conducted in triplicate with 200 μL of human plasma at 37 °C and 0.1 μg–6 μg of enzyme. As a control, plasma (200 μL) devoid of the enzyme was used. Fibrinogenolytic activity was determined using the method described by Edgar and Prentice (1973) with modifications as indicated by Rodrigues et al. (2000). Samples of bovine fibrinogen (20 μg) dissolved in a buffer (0.1 M Tris–HCl AG-014699 ic50 pH 7.4, 0.01 M NaCl) were incubated with different concentrations of each enzyme (0.05–1.0 μg) at 37 °C for 30 min. The reaction was stopped by the addition of a reducing buffer (10% (v/v) glycerol, 10% SDS, 5% 2-mercaptoethanol, and 0.05% (w/v) bromophenol blue). Fibrinogen hydrolysis was evidenced by 12% SDS–PAGE gels. The fibrinolytic activity was performed as described by Leitao et al. (2000) with some modifications. A 0.3% fibrinogen solution was prepared in barbital buffer (50 mM Oxalosuccinic acid sodium barbital, 1.66 mM CaCl2, 0.68 mM MgCl2, 94 mM NaCl, 0.02% sodium azide, pH 7.8) and added to 0.95% agarose in barbital buffer under heating,

until the formation of a transparent colloid. Upon cooling, the agarose solution (40 °C) was added to the solution of fibrinogen (fibrinogen: agarose, 1:1, v/v). 100 μL of bovine thrombin (1 μg/μL) was added to the solution, which was then poured into a Petri plate for clotting and fibrin formation. The samples were applied to pores in the gel at the desired concentrations (4–64 μg) in a final volume of 30 μL, followed by incubation at 37 °C for 24 h and subsequent measurement of the haloes. The experiment for proteolytic activity was carried out by using the method described by Sant’ Ana et al. (2008) with some modifications. Here, the pH was varied instead of the concentration of the protein. Casein solution (1% w/v) was prepared in different pHs (4.6, 5.4, 6.2, 7.0, 8.0, 8.6 and 10.2).

The iteration with the lowest root mean square error (RMSE) is chosen and denoted as H^sr∗. Typically,

r∗r∗ is around 4. Hs(t=0,m)=0Hs(t=0,m)=0 is assumed when applying Eq. (19) to simulate HsHs. One important assumption in regression analysis is that the residuals ( ε(t)=Hs(t)-H^s(t) in this case) are Gaussian distributed. This assumption is violated here, because in theory Hs(t)Hs(t) are non-negative data, which are obviously non-Gaussian. The consequences of such violation could tender the model performance, even resulting in nonsense values such as H^s<0. To evaluate the effects of violation of the Gaussian assumption on the model performance, and to improve the model performance, we explore two options for transforming the positive data (actually, both G   and HsHs are all positive values):

(i) the log transformation (noted as trlntrln in Table 4), which has been used by others Wortmannin (e.g. Casas-Prat and Sierra, 2010 and Ortego et al., 2012); and (ii) the Box–Cox power transformation (noted as trbctrbc in Table 4 and Eq. (21)) ( Sakia, 1992), which also includes the log transformation as a special case (the case of λ=0λ=0) and has recently been applied by Wang et al. (2012): equation(21) trbc(X)=ln(X)ifλ=0,(Xλ-1)/λotherwise,where X   denotes a variable of positive values. The parameter λλ is chosen so that the departure of X from a Gaussian distribution is minimized. As detailed in Table 4 (Settings 6–8), we apply these transformations to the Natural Product Library cost Oxymatrine predictand (HsHs) alone, and to both HsHs and the non-Gaussian predictor G (before calculating the anomalies and deriving the principal components, but after calculating the direction of the SLP gradient). The resulting model performance is compared later in Section 5. The statistical model is calibrated

and validated with HIPOCAS data (1958–2001) (see Section 3.1), which is split into two non-overlapping subsets: 1971–2000 for model calibration, and 1958–1970 for evaluation of model performance. We use the HIPOCAS data for the period 1971–2000 (calibration period) to calibrate the statistical model, namely, to estimate the unknown parameters in Eq. (2), including aˆ,aˆP,aˆG,aˆEOF+,i,aˆEOF-,i and αˆr∗ (see Eqs. (2), (15) and (19) and Fig. 5). This 30-year period is also chosen as the baseline period to derive the climate model simulated baseline climate for use to infer projected future changes in HsHs (see Section 3.2). Then, we use the HIPOCAS data for the period 1958–1970 (validation period) to evaluate the performance of the above calibrated statistical model. The validation considers the following three aspects: (i) overall model performance, (ii) model skill for a range of different quantiles of wave heights, and (iii) model errors in modeling waves along the Catalan coast. Note that all anomalies in this study are relative to the climatological mean field of the baseline period (1971–2000).

1067G > A (p.G356D). This mutation has previously been reported in other FOP variant patients [7] and [25]. The R206H mutation may cause all three clinical types of FOP including classic FOP, FOP-plus and FOP variants. In this large patient series, all classic FOP and FOP-plus patients and one FOP variant carried the R206H mutation. Two FOP variant cases had non-R206H mutations. This phenomenon is consistent with a previous report [7] which only detected

non-R206H mutations in variant FOP patients. None of the 98 unaffected controls, including parents and siblings, had mutations in ACVR1. Penetrance of the ACVR1/ALK2 mutation was 100%. The parents of the FOP patients could recall the onset and features of flare-ups in all cases. In this study, the onset of FOP was considered to be the time when the first spontaneous flare-up appeared or the first HO lesion emerged after trauma. Sixty-nine percent of patients (50/72 cases) experienced the spontaneous Bortezomib in vitro onset of flare-ups. Thirty-six percent of patients (18/50 cases) experienced the spontaneous onset of a flare-up prior to two years of age; 58% of patients (29/50 cases) experienced the spontaneous onset of a flare-up between two and ten years of age; and 6% of patients (3/50 cases) experienced the spontaneous onset of a flare-up after age 10.

There selleck chemicals llc was no significant difference between male and female patient’s distributions among various onset ages (Table 2). No patient with spontaneous onset of FOP had any premonitory signs or symptoms prior to the onset of a flare-up. The signs and symptoms accompanying the onset of a flare-up were different at different anatomic sites. If the flare-up was in the head, neck or trunk, the onset was usually acute with large painless or painful soft masses appearing within twelve hours. If the flare-up involved the extremities, patients were more likely to have had focal pain with decreased range of motion as their nearly initial complaint, with or without the appearance of soft tissue swelling. Fifty-two percent of patients (26/50 cases) who experienced spontaneous onset of flare-ups presented with soft tissue swellings in the occipital region. Typically, as one mass subsided,

another one emerged and sequentially spread toward the back of the neck and trunk. Most masses eventually ossified, but some resolved completely. Twenty-three of the 26 patients who had spontaneous occipital masses had radiographic evidence of HO in the occipital and posterior neck regions at the first visit to our clinic, but three of the 26 patients who had reported flare-ups in the occipital region had no radiographic evidence of HO in the occipital region, although these three patients had HO at other sites where intercurrent flare-ups had occurred. Forty percent of patients (20/50 cases) with spontaneous onset of FOP presented with soft tissue swelling or focal edema in the neck, back, trunk or shoulder, and all of the soft tissue masses become ossified.

ChipCE–MS systems need further improvements in robustness before they can be applied on a larger scale. Work is currently focussed on make-up flows, which we expect to lead to more robust systems. Lastly, we foresee increasing attention for coupling in vitro cell models (such as organ-on-a-chip and 3D cell culture) to MS. Pharmaceutical companies are increasingly interested to make

use of such devices to gain additional information efficacy and toxicity of their compounds in the discovery and pre-clinical stage. Papers of particular interest, published within the period of review, have been highlighted as: • of special interest We would like to express our gratitude to Vincent van Duinen for the creation of the graphical abstract. This work was made possible by the European Union STATegra project, EU FP7 grant number Belnacasan nmr 30600. “
“Current Opinion in Biotechnology 2015, 31:101–107 This review comes from a themed issue on Analytical biotechnology Edited by Hadley D Sikes and Nicola Zamboni http://dx.doi.org/10.1016/j.copbio.2014.08.005 0958-1669/© 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC

BY license (http://creativecommons.org/licenses/by/3.0/). There is an intrinsic drive for biological entities to cooperate and coordinate responses to environmental queues. From DNA replication to bacterial quorum sensing, through to bird flock behaviours, and even in human economical structures, biological systems organise behaviours via communication. Signals by themselves do not usually contain any meaning, i.e. supplying Pifithrin-�� nmr useful patterns, materials or energy. Rather, meaning

appears only when the agents involved in communication interpret the information. But how can we in the life sciences quantify this information? The mathematical formulation of communication systems and information was laid down by Claude Shannon in a landmark 1948 paper [1]. Shannon showed that axiomatic rules describe and predict communication between a sender and a receiver, establishing limits in mutual information transfer imposed by the channel in which a message is transmitted. The beauty of Shannon’s work is that it applies to any system that can be abstracted to a sender–receiver (S–R) topology. S–R systems use the ‘bit’ as the unit of information, ADAMTS5 and this is the ratio of the probability of a state, given that a signal has been received, versus the probability of a state without a signal. In other words, the quantity of information in a signal can be measured by the shifts in state probabilities. However, some researchers argue that it is equally important to have a measure for the context or ‘meaning’ of a signal as well as the quantity [2]. In this review, we will focus on studies relating to S-R systems with cells and biomolecules as the information processing agents.

1B1, lanes 2 and 3) were both transferred to a PVDF membrane

and submitted to Edman degradation. The first 34 amino acid residues from N-terminal sequencing of the reduced protein were determined to be LGPDIVSPPVCGNELLEVGEECDCGTPENCQNE (Fig. 2) and submitted to BLAST. The 10 first amino acids residues of the non-reduced moojenin obtained by Edman degradation showed the same sequence as the reduced moojenin (data not shown). The primary Gefitinib solubility dmso sequence of the reduced moojenin shared a high degree of identity with proteins of the PIIIb subclass of SVMPs, except for a proline (Pro208) where threonine (Thr208) is observed in other known sequences. This sequence begins at the spacer region in other members of the PIIIb subclass of SVMPs (residue 206 – numbering according to Jararhagin), such as the disintegrins Catrocollastatin-C (Calvete et al., 2000) and Jararhagin-C (Usami et al., 1994), suggesting that the moojenin had undergone autolysis. Subclass PIIIb metalloproteinases can undergo proteolysis/autolysis during secretion or in the check details venom to generate disintegrin-like and cysteine-rich domains (DC domain) (Fox and Serrano, 2005). However, no proteinase domain released from the DC domain of a PIIIb metalloproteinease has been isolated intact from snake venom, since they are apparently unstable alone (Shimokawa et al., 1997; Moura-da-Silva

et al., 2003; Fox and Serrano, 2005 and Fox and Serrano, 2008). A spacer region, or linker, separates the M from the DC domain and includes a proteolytic site (Moura-da-Silva et al., 2003; Assakura et al., 2003; Muniz et al., 2008), but the cleavage site is not yet known. It has been observed that proteolytic processing occurs in the spacer domain in some members of each of the P classes Pyruvate dehydrogenase lipoamide kinase isozyme 1 (Fox and Serrano, 2005). For example, processed PIIIb catrocollastatin-C (Fig. 2) has a spacer region linked to the disintegrin-like domain just as in reduced moojenin (Fox and Serrano, 2005), while native jararhagin-C (Usami et al., 1994) and ALT-C (Souza et al., 2000) contain only the DC domain. Other members of the PIII class undergo autolysis

under non-physiological conditions in vitro ( Takeya et al., 1993); however, the products of this proteolytic processing are not observed by SDS-PAGE under non-reducing conditions, suggesting these domains are connected by disulfide bonds ( Moura-da-Silva et al., 2003). Moreover, under reducing conditions the DC domain was seen without the M domain, since the latter alone is unstable. Other members of the PIII subclass can be manipulated to undergo in vitro autolysis, but the relevance of this processing in vivo is unclear ( Fox and Serrano, 2005). Under physiological conditions, Moojenin probably maintains both native and processed conformations, since the sequence determined for non-reduced moojenin begins at the spacer region.

The sequences of the forward and reverse primers were as follows: GAPDH — ACCACAGTCCATGCCATCAC and TCCACCACCCTGTTGCTGTA, PCR product size 452 bp [19]; Runx2 — ATGCTTCATTCGCCTCACAAAC and CCAAAAGAAGTTTTGCTGACATGG, PCR product size 261 [20]; Osteocalcin — ACACTCCTCGCCCTATTG and GATGTGGTCAGCCAACTC,

PCR product size 249 bp [21]. The thermal cycle conditions were 95 °C for 4 min followed by 40 cycles of 30 sec at 95 °C , 1 min at 55 °C and 30 sec at 70 °C. All assays were performed in triplicates. Averaged cycle of threshold (Ct) values of GAPDH triplicates were subtracted from Ct values of target genes to obtain ΔCt, and then relative gene expression was determined as 2− ΔCt. The results were presented relative to the control value, which was arbitrarily set to 1. Cells were lysed in lysis buffer (30 mM Tris–HCl pH 8.0, 150 mM NaCl, 1% NP-40) containing 1 mM phenylmethylsulfonyl fluoride and protease inhibitor Dabrafenib mw cocktail (both from Sigma-Aldrich, St. Louis, MO) on ice for 30 min, selleck kinase inhibitor centrifuged at 14000 g for 15 min at 4 °C, and the supernatants were collected. Equal amounts of protein from each sample were separated by SDS-PAGE and transferred to nitrocellulose membranes (Bio-Rad, Hemel Hempstead, UK). Following incubation with primary antibodies against Runx2, bone morphogenetic protein 2 (BMP2) (both from Invitrogen, Carlsbad, CA), microtubule-associated protein 1 light-chain 3β (LC3β),

phospho-AMPKα (Thr172), AMPKα, phospho-Akt (Ser473), Akt, phospho-mTOR (Ser2448), mTOR, phospho-Raptor Silibinin (Ser792), Raptor, phospho-p70 S6K (Thr389), p70 S6K, beclin-1, actin (all from Cell Signaling Technology, Beverly, MA) or p62 (Biolegend, San Diego, CA), and peroxidase-conjugated goat anti-rabbit IgG (Jackson ImmunoResearch Laboratories, West Grove, PA) as the secondary antibody, specific protein bands were visualized using Amersham ECL reagent (GE Healthcare, Pollards Wood, UK). The protein levels were quantified by densitometry using Image J software and expressed relative to actin (Runx2, BMP2, LC3-II, beclin, p62) or corresponding total protein

signals (phospho-AMPK, phospho-Akt, phospho-mTOR, phospho-Raptor, phospho-p70 S6K). The intensity of phospho-AMPK signal in AMPK-knockdown cells and phospho-mTOR signal in mTOR-knockdown cells was expressed relative to actin. The signal intensity values are presented below the relevant bands. HDP-MSC stably expressing control lentiviral vector plasmids or plasmids encoding human AMPKα1/2 or LC3β short hairpin RNA (shRNA) were generated according to the manufacturer’s instructions (Santa Cruz Biotechnology, Santa Cruz, CA). Small interfering RNA (siRNA) targeting human mTOR and scrambled control siRNA were obtained from Santa Cruz Biotechnology (Santa Cruz, CA). Subconfluent hDP-MSC were transfected with mTOR or control siRNA according to the manufacturer’s protocol. Cells were allowed to grow 24 h following transfection, at which point the differentiation medium was added.