Deductive biology—Some cautious steps

For certain environments, the Darwinian model allows unique prediction of a function that any surviving system adapted to such an environment has to perform. This is the case for those environments that determine a “survival functional” of position in space-time of known shape. Purely temporal survival functionals can be distinguished from spatial and mixed ones. In each case, there exists an optimum path in combined physical and (reduced) metabolic space. Dependent on the admissible error, approximate solutions of different complexity are sufficient. All solutions possess an afferent, a central, and an efferent part. Within this general frame, specific, “probably simplest”, solutions are proposed for adaptive chemotaxis, insect locomotion, lower vertebrates locomotion, higher vertebrates locomotion, chronobiological systems, and immune systems, respectively—or rather, for the underlying functionals. Presented at the Society for Mathematical Biology Meeting, University of Pennsylvania, Philadelphia, August 19–21, 1976.     

Some Salt with Your Statin,Professor?

We know that clinical trials sponsored by the pharmaceutical industry are likely to exaggerate benefit and minimise harms. But do these biases extend to their sponsorship of non-human animal research? Using systematic review and meta-analysis Bero and colleagues show that, in the case of statins, things are a little more complicated. While the conclusions of industry-sponsored studies were indeed more enthusiastic than warranted by their data, the data themselves painted a picture more conservative than was seen in non-industry-sponsored studies. This behaviour is consistent with maximising the return on investment, seeking robust data before embarking on a clinical trial, and, once that investment has been made, making every effort to “prove” that the drug is safe and effective if this is at all credible. The findings suggest that there is something different about industry-sponsored non-human animal research, perhaps reflecting higher standards than is the case elsewhere. Perhaps the academic community can learn something from our colleagues in the commercial sector.It is now pretty clear that, in clinical trials, sponsorship from the pharmaceutical industry is associated with substantial and important overstatement of how effective drugs are, and with understatement of adverse effects [1]. Of course, these are average effects, and so are insufficient to label the whole industry bad. Nonetheless, there are many examples where industry has been shown to seek to subvert rational interpretation of trial data to influence guideline development and prescribing behaviour [2]–[4]. These examples lead to the reasonable conclusion that findings from trials sponsored by the pharmaceutical industry need to taken with more salt than is probably good for you.What then of other research used to inform the drug development process? What of the in vitro and in vivo non-human research supported by industry, either in companies'' own laboratories or that companies fund in contract research organisations or in academic collaborations? Are the findings of such studies credible? And how do those findings compare with “proper” research conducted by dispassionate academics?These are important questions, but how could we find this stuff out? In the same way that it would be difficult to conduct a randomised controlled trial of the effect of living in Scotland on your chance of having a stroke, it is difficult to do an experiment to test whether the funding source for a study influences the outcome. We have to rely on observational (rather than experimental) research, and we need to be much more cautious in our approach and in our conclusions.Over the last few years there has been a big increase in the use of such an observational approach to better understand the strengths and weaknesses of different research domains. The Cochrane Collaboration began as an attempt to give reliable summaries of the effectiveness of treatments in human clinical trials [5], but along the way the data collected have also allowed investigation of whether studies with certain characteristics tended to give overstatement or understatement of these summary treatment effects [6]. The insights arising from this approach, and the improvements in trial design that they have driven, are just as important as the improved information to guide treatment decisions. This approach has been used by others—notably Lisa Bero, the senior author of the research article presented here—in a series of important papers that identified the prevalence and impact of funding bias in human research [7],[8].Those wishing to study, and to improve, other research domains such as non-human animal research have been able shamelessly to borrow from the experience of the Cochrane Collaboration. Using a systematic approach to data retrieval we can assemble an unbiased cohort of relevant studies, then observe associations between different aspects of experimental design and the magnitude of the effects reported. What we''re looking for are design features that are consistently associated with either under- or overestimation of biological effects.Of course, meta-analyses of clinical trial data put together a small number of large studies measuring a common treatment effect, whereas in animal studies there is usually a large number of small studies measuring different effects (dose, stage of illness, different animals), which means the approach used has to be adjusted slightly, but still, the approach has been fruitful.For a large number of non-human animal disease models, studies at risk of bias (for example, those without randomisation or blinding) give larger estimates of treatment effects [9]–[13]; the majority of studies are at risk of bias [9]–[14]; and journal impact factor is no guarantee of low risk of bias [15]. These findings influenced the development of reporting standards for stroke [16] and non-human animal research more generally [17],[18], and these are beginning to make an impact.One difficulty in using meta-analysis is in working out how to combine different outcome measures, often from different animals. A 0.1-mm increase in aortic arch atheroma is probably less important in a Scot than it is in a mouse, so we need to transform data onto a common scale. In standardised mean difference (SMD) meta-analysis, the effect is standardised to the observed variance [19]. Because—in large studies at least—this variance is a property of the biology being studied rather than of the scale being used, it allows effects to be converted to a common scale. So, by way of an example: in 2012 the variance of the monthly average temperature across 258 weather stations in California was 12.55°F, or 6.98°C—from which we can calculate that 1°C is the same as 1.80°F, or 0.14 standardised units, and so we have a common scale.While this approach is very useful in clinical meta-analyses (where the large number of participants in each group allows a precise estimate of the population variance), it becomes less useful where group size is small, because here the observed variance is a less precise estimate of the population variance. This introduces a measurement error to the conversion between different scales.Further, this observed variance represents a combination of underlying biological variation in the phenomena being measured and of variation arising from measurement error and from the way the experiment was performed. Experiments with low measurement error and good protocol compliance will therefore have lower aggregate variance than those with high measurement error and poor protocol compliance. Since the variance is the denominator in the calculation of the size of differences between groups, any given effect size will be artificially larger in studies with low measurement error and experimental variability.The demonstration that experiments with low methodological quality can give inflated estimates of treatments effects, and that most experiments appear to be of low methodological quality, leads to the question of who might be the worst offenders. Since clinical trials sponsored by the pharmaceutical industry seem to be at greater risk of bias than others, a lazy assumption might be that their non-human animal research is similarly confounded, as they seek to rush compounds to market to maximise profitability.However, a few straws in the wind hint this might not be the case. One way companies identify drug targets is by reading what''s out there in the literature and, if something looks interesting, seeking to replicate the findings. Bayer scientists found inconsistencies in 43 of 65 studies when they tried to replicate them in-house [20]. Scientists in the haematology and oncology departments at Amgen were able to replicate findings in only six out of 53 publications identified as “landmark” studies [21]. When the ALS Therapy Development Institute tried to replicate published findings of drug efficacy in the superoxide dismutase mouse model of motor neuron disease (amyotrophic lateral sclerosis), not one of seven interventions retained efficacy [22]. Implementation of good laboratory practice standards is much more advanced in industry labs, and for some types of experiments these standards are a legal requirement. Indeed, a scientific researcher was recently jailed in Scotland for research fraud [23]. So, could it be that industry-sponsored research is actually more rigorous than academic research?Taking the example of statin treatments for atheroma, David Krauth, Andrew Anglemyer, Rose Philipps, and Lisa Bero address this issue head-on [24]. Using systematic review they identified non-human animal studies describing the efficacy of statins. Their methodology is secure, with an a priori analysis plan, clear inclusion and exclusion criteria, and duplicate extraction of key variables from identified publications. They found low levels of reporting of measures known to reduce the risk of bias, with blinded assessment of outcome reported in only 22 of 49 studies, and no studies reporting full randomisation or a sample size calculation. Reassuringly, the quality of reporting seems to have improved somewhat since publication of the ARRIVE guidelines in 2010. However, there is still clearly a long way to go.On the question of the influence of the study sponsor, Bero and colleagues identified 19 studies sponsored in whole or part by industry, 28 sponsored by non-industry sources, and 16 with no statement of sponsorship or a statement of no sponsorship. Focussing on those studies where sponsorship status was known, they found that the results of nine of 19 industry-sponsored studies (43%) and 18 of 28 non-industry-sponsored studies (72%) supported the efficacy of statins. This finding was confirmed in a subset of 38 studies with sufficient data to allow meta-analysis; statins were reported to improve outcome by 0.73 SMD units in industry-sponsored studies, while in studies with other sponsorship the improvement was 1.99 SMD units. This difference is highly significant—I calculate an excess of efficacy in non-industry-sponsored studies of 173% (95% confidence interval 52% to 293%). Put simply, studies with non-industry sponsorship report that statins are almost three times more effective than do industry-sponsored studies.As interesting, however, is the analysis of the interpretation placed on the findings in each of the included studies. Of 19 industry-sponsored studies, the conclusion of 18 favoured the use of statins (95%), while of 28 non-industry-sponsored studies, only 21 did so (75%). This is striking for two reasons: first, in both cohorts the conclusion appears to be more enthusiastic than the findings presented, and second, this phenomenon appears to be much more marked in studies with industry sponsorship.So what''s going on? Of course, these observed differences may be due to some other, unmeasured difference between the contributing studies, but the analyses were prespecified and such a confound appears unlikely. If industry-sponsored studies were of consistently larger variance, then the effect sizes observed would appear smaller in SMD units, but there is no reason to suspect that this was the case here.It does therefore appear that findings from research sponsored by industry are more conservative than those sponsored by non-industry sources, but the interpretation of those data is, in contrast, less conservative. Why might this be?In a series of univariate analyses the authors examined the impact of three factors—randomisation, blinding, and accounting for all animals—that might increase the risk of bias. Even when these were taken into account, non-industry-sponsored studies gave significantly higher estimates of efficacy, implying that some other factors were responsible. This might happen if “randomisation” and “blinding” meant different things in industry-sponsored studies, or through the impact of some other, unmeasured risk of bias, or through some gestalt of industry-sponsored studies that is not described by the variables tested. Alternatively, academic studies exploring pathophysiology might chose circumstances that maximise the observed effect size, to give greater statistical power to experiments testing inhibition of those effects.In my view it is likely that the impact of approaches to research management and the regulatory environment that apply to some parts of industry—particularly standards for internal reporting—extends to most of the non-human animal research activity with which they are involved, whether or not it is performed in-house. That is, non-human animal work sponsored by industry is likely to be performed and reported to a higher quality, and to be at lower risk of bias, than work sponsored by others. This would explain the difficulty industry has in replicating the results of research conducted in academic labs. However, the interpretation, or “spin”, with which industry-sponsored work is presented does appear to be an issue, with exaggeration of the conclusions to favour the drug being tested.This makes sense—for industry there is a clear financial interest in being absolutely secure in the non-human animal data for a compound before embarking on a clinical trial, so there is a real motivation to get the preclinical data as good as they can be. Clinical trials are expensive, and so it is worth investing much time and effort, and perhaps even funding multicentre “phase 3” animal studies [25]–[27], to maximise the prospects for success. But when that money has been spent (and for statins it largely has been), the motivation is to present an analysis of the available data that is most supportive for clinical use. So, if a drug is a turkey, try to find that out before spending a fortune taking it to clinical trial—and if it''s too late for that, try to convince everyone that the non-human animal and clinical trial data supporting an efficacy for Meleagris gallopavo (commonly known as the wild turkey) are more convincing than they might at first appear.In contrast, academic researchers are rewarded not for the marathon but for the sprint—for a high-impact publication describing a part of the jigsaw, not for the body of work that shows the whole picture. To them, substantial efficacy in a single study is, in some respects, an end rather than a beginning.Bero and colleagues have made an important contribution; their findings suggest that academic researchers might learn good practice in the management, conduct, and reporting of non-human animal research from colleagues in industry, and reinforces the importance for readers of research reports to focus on methods and data rather than on abstracts and conclusions.     

Some properties of a fungal β-D-glucanase preparation

A commercial enzyme preparation, of fungal origin, contained a mixture of β-D-glucanases which were fractionated by ion-exchange chromatography to give a mixture of an endo-(1→4)- and an exo-(1→3)-β-D-glucanase. These two enzymes were then separated by molecular-sieve chromatography on Sephadex G-150. The purified exo-(1→3)-β-D-glucanase has a relatively high specificity for (1→3)-β-D-glucosidic linkages, and has no action on lichenin.     

Some proteins keep “living fossil” pre-sequence

The sequences of hydrophobic segments of exported bacterial proteins, some serine proteinases and all known plastocyanins are examined in order to find out subsequences differing in amino acid composition and primary structure regularities. It is established that the extension in protein precursor, cleaved by a proteolysis (so-called P-sequence), demonstrates a higher share of usual amino acids (Thr, Pro, Ala, Ser, Arg, Gly, Leu, Val, Glu, Asp) and more clearly expressed periodicity compared to the mature protein (M-sequence). The obtained results confirm the conception of primitive proteins comprising a small number of amino acids realized a preferable bonding (between identical and very similar in structure-function-evolution sense).     

Survey of Some Actinomycetales for α-Galactosidase Activity

The enzyme alpha-galactosidase offers potential to (i) eliminate possibly the flatus-inducing factor(s) in edible beans, (ii) eliminate raffinose during beet-sugar processing, and (iii) determine raffinose analytically. Accordingly, 20 genera of the order Actinomycetales Buchanan 1917 were tested for evidence of alpha-galactosidase activity. Test filtrates were prepared with a medium containing D-galactose and soybean meal. Enzyme activity was demonstrated through cellulose thin-layer chromatography. Of 123 strains tested, 28 produced extracellular alpha-galactosidase. Almost all were streptomycetes. Members of the genera Actinoplanes Couch 1950, Micromonospora varphiOrskov 1923, and Promicromonospora Krasil'nikov et al. 1961 also exhibited alpha-galactosidase activity. Additional tests led to the selection of five strains whose filtrates degraded melibiose, raffinose, and stachyose but not lactose and sucrose. Tests also were made with several soybean preparations.     

Some psychophysical determinants of discrete Moiré patterns

In a series of experiments we have investigated the perception of Moiré patterns as a function of spatial density, rotation and temporal display parameters. Results indicate that the local correlation extraction process involved in the perception of these patterns is not feature specific, yet is driven by excitatory (correlated) and inhibitory (uncorrelated) information under a form of spatial summation. These results are comparable with recent results on texture discrimination where texture interpoint distance distributions (dipole statistics) have also been discovered to have excitatory and inhibitory components.     

Clinical Neurology—Some Applications of Scanning by Computer

The use of averaging techniques permits a great increase in the resolution of time-locked waveforms in the electroencephologram.Averaged evoked potentials may be used to study sensory-sensory interactions and the effect of irritative and destructive lesions on the average evoked potential.More complex computations are possible once averages are derived, as was shown in the application of potential contour mapping, where considerable differences were noted between premature and newborn infants on the one hand and adults on the other.The flat, low voltage surface potential contour of the neonate was interpreted as reflecting a volume-conducted event having a distant, deep and midline source. Potential contour patterns in the adult were interpreted as showing predominantly neuronally propagated activity arising at the cortical surface.     

Low Resolution Mass Spectra of Some Unsaturated δ-Lactones

Mass spectra of the δ-lactones of the following 5-hydroxy-2-enoic acids were determined: 5-hydroxyhex-2-enoic acid (I), 5-hydroxyoct-2-enoic acid (II), 5-hydroxydec-2-enoic acid (III), 5-hydroxydodec-2-enoic acid (IV), 5-hydroxy-8-methylnon-2-enoic acid (V), 5-hydroxy-6-ethyloct-2-enoic acid (VI), 5-hydroxy-5, 6, 6-trimethylhept-2-enoic acid (VII), and 5-hydroxy-5-methylnon-2-enoic acid (VIII). The following modes of fragmentation are consistent with observed m/e values, metastable peaks, and established modes of breakdown in compounds containing similar atomic groupings:—1. Loss of side chain, resulting in ions at m/e 97 for I-VI and at m/e 111 and 153 for VII and VIII (diagnostic peaks); 2. 1,4-Rupture of the ring giving an ion at m/e 68 (diagnostic peak) which loses CO to give m/e 40; 3. Loss of CO from m/e 97 fragment to give m/e 69 which breaks down further to m/e 41→m/e 39; 4. 1, 4-Rupture of m/e 111 and m/e 153 fragments to give m/e 43 and 85, further breakdown of m/e 85→57→41→39; 5. Loss of H₂O from the molecular ion providing there is a hydrogen atom on C₅ and the side chain is at least 3 carbon atoms in length, further loss of H₂O when the side chain is equal to C5 or C7; 6. Loss of CO₂ from the molecular ion of I, IV-VIII; 7. Loss of CO from all molecular ions; 8. Loss of 2×28 from the molecular ions of III, IV, V, VI; 9. Loss of (18 + 28) from the molecular ion of III, IV, V, VI; 10. Loss of 60 from the molecular ion of II, III, IV, V, VI; 11. Formation of M + 1 ion (169) of VII and VIII; 12. Formation of M + 1 ion (143) of saturated δ-octalactone and loss of H₂O from this M + 1 ion.     

Some properties of rat-liver glucose–adenosine triphosphate phosphotransferases

1. Bacilysin, a peptide which yields l-alanine and l-tyrosine on acid hydrolysis, was produced by a strain of Bacillus subtilis (A 14) in a chemically defined medium containing glucose, ammonium acetate or ammonium chloride, potassium phosphate and other inorganic salts, and ferric citrate. 2. Under the conditions used growth was diphasic. Bacilysin was formed during the second phase of slower growth, and there was little production during the stationary phase. Nevertheless, bacilysin production occurred when protein synthesis was inhibited by chloramphenicol. It thus appears that there is no obligatory coupling of protein synthesis and bacilysin synthesis. 3. When dl-[1-(14)C]alanine was added to a growing culture of B. subtilis, (14)C was incorporated into bacilysin, which contains an N-terminal alanine residue. 4. Under similar conditions virtually no (14)C was incorporated into bacilysin from dl-[2-(14)C]tyrosine, l-[U-(14)C]tyrosine or [1-(14)C]acetate, although these compounds were used by the cell for the biosynthesis of other substances. These results indicate that neither tyrosine nor acetate is a precursor of the fragment of bacilysin which yields tyrosine on hydrolysis with hot 6n-hydrochloric acid. 5. The tyrosine-yielding fragment of bacilysin was labelled with (14)C from [1,6-ring-(14)C(2)]shikimic acid. The biosynthesis of bacilysin thus appears to involve a diversion from the pathway leading to aromatic amino acids at the shikimic acid stage, or a subsequent one.     

Purification and Some Properties of Chaetomium trilaterale β-Xylosidase

A β-xyloside hydrolytic enzyme of the fungus Chaetomium trilaterale was further purified by a modification of Kawaminami’s procedure (DEAE-Sephadex A-25 and Sephadex G-75 column chromatography), followed by isoelectric focusing. The purified preparation was homogeneous by polyacrylamide disc gel electrophoreses at pH 4.3 and pH 8.3. The purified enzyme hydrolyzed β-d-glucopyranosides as well as β-d-xylopyranosides, and the ratio of β-glucosidase activity against β-xylosidase activity increased about 3 fold during the purification steps. The molecular weight of this preparation was estimated to be about 240,000 by Sephadex G-200 gel filtration and 118,000 by SDS-polyacrylamide slab gel electrophoresis. The isoelectric point was 4.86 and the amino acid composition was also determined.

The optimum pH was at 4.2 for phenyl β-d-glucoside and around 4.5 for phenyl β-d-xyloside. The β-xylosidase activity was relatively stable but β-glucosidase activity was rapidly inactivated, at the alkaline pH range above 11. The heating of the preparation at 60°C didn’t show a parallel inactivation of the two activities. N-Bromosuccinimide strongly inactivated both enzyme activities. Nojirimycin and glucono-l,5-lactone showed a stronger inhibition on β-xylosidase activity than on β-glucosidase activity. The maximal velocities decreased in the order; phenyl β-d-glucoside > cellobiose > phenyl β-d-xyloside > xylobiose; the value with phenyl β-d-glucoside was about 28-fold higher than that with phenyl β-d-xyloside.     

Purification and Some Properties of Saccharomyces logos α-Glucosidase

α-Glucosidase was purified from Saccharomyces logos by precipitation with ethanol, and chromatographies on Sephadex G–200, DEAE-Sephadex, DEAE-ceiluiose and Duolite A–2. The purified α-glucosidase was homogeneous on ultracentrifugation and zone electrophoresis using cellulose acetate membrane. The sedimentation coefficient was calculated to be 9.6 S. The molecular weight was estimated to be approximately 2.7 × 10⁵ by gel-filtration technique.

The optimum pH was found to be in the range of 4.6~5.0, and the optimum temperature was 40°C. The enzyme exhibited higher hydrolytic activity toward maltose rather than toward phenyl-α-glucoside and turanose, and no activity toward sucrose.

The enzyme was a glycoprotein containing carbohydrate of about 50%.     

Some Properties of Transglycosylation Activity of Sesame β-Glucosidase

The transglycosylation reaction of partially purified β-glucosidase from sesame seeds with cellobiose is described. Sesame β –glucosidase was partially purified by ammonium sulfate fractionation and gel filtration. The molecular weight of the enzyme was 200,000 by gel filtration. Sesame β-glucosidase showed strong transfer activity to synthesize the trisaccharide from cellobiose. The optimum pH and temperature of the transglycosylation reaction were pH 4.0 and 60°C.     

Physicians' Own Health—Some Advice for the Advisors

Information about physicians'' health and health practices is sparse and scattered. With a few exceptions, however—notably suicide and substance abuse—it appears that physicians'' health and health-promotion activities are at least similar to those of the general public. In some areas, such as smoking cessation, physicians have far outstripped the general public. As physicians gain more insight into their own health and health habits, advice to patients can be realistic and effective. Indeed, several personal health activities, including immunization, have direct, salutary impacts on patient care. Physicians should analyze and change their own health practices as indicated and pay special attention to “high yield” health habits, such as seat-belt use.     

Purification and Some Properties of Flint Corn α-Glucosidase

An α-glucosidase was purified from flint corn by precipitation with ammonium sulfate, chromatographies on CM-cellulose and Hydroxylapatite and gel-filtrations on Sephadex G-100. The purified enzyme was homogeneous in ultracentrifugal and disc electrophoretic analysis. The sedimentation coefficient was calculated to be 6.5 S. The molecular weight was estimated to be approximately 6.5×10⁴ by gel-filtration technique.

The optimal pH was found to be 3.6 for both maltose and soluble starch. The enzyme lost about 80% of the activity by incubation at 60°C for 10 min.

The ratio of velocity of hydrolysis for maltose, phenyl-α-glucoside and soluble starch was estimated to be 100:14.3:6.1 in this order. The αglucosidase hydrolyzed soluble starch exo-wisely.     

Why Are Some Plant Genera More Invasive Than Others?

Determining how biological traits are related to the ability of groups of organisms to become economically damaging when established outside of their native ranges is a major goal of population biology, and important in the management of invasive species. Little is known about why some taxonomic groups are more likely to become pests than others among plants. We investigated traits that discriminate vascular plant genera, a level of taxonomic generality at which risk assessment and screening could be more effectively performed, according to the proportion of naturalized species which are pests. We focused on the United States and Canada, and, because our purpose is ultimately regulatory, considered species classified as weeds or noxious. Using contingency tables, we identified 11 genera of vascular plants that are disproportionately represented by invasive species. Results from boosted regression tree analyses show that these categories reflect biological differences. In summary, approximately 25% of variation in genus proportions of weeds or noxious species was explained by biological covariates. Key explanatory traits included genus means for wetland habitat affinity, chromosome number, and seed mass.     

Purification and Some Properties of β-Mannanase from Penicillium purpurogenum

A β-mannanase was purified from the culture filtrate of Penicillium purpurogenum No. 618 by 1st and 2nd DEAE-cellulose column chromatographies, and subsequent Ultro-gel chromatography. The final preparation thus obtained showed a single band on polyacrylamide disc-gel and SDS-polyacrylamide gel electrophoresis. The molecular weight and isoelectric point were determined to be 57,000 and pH 4.1 by SDS-polyacrylamide gel electrophoresis and isoelectric focusing, respectively. The purified mannanase contained the following amino acids: glycine > serine >glutamic acid > alanine > aspartic acid. The mannanase exhibited maximum activity at pH 5 and 70°C, and was stable in the pH range of 4.5 to 8 and at temperatures up to 65°C. The enzyme activity was not affected considerably by either metal compounds or ethyl- enediaminetetraacetic acid. Copra galactomannan (Gal: Man =1 :14) was finally hydrolyzed to galactose, mannose and β-1,4-mannobiose through the sequential actions of the purified mannanase and the α-galactosidase purified from the same strain.     

A Further Separation and Some Properties of β-Lactamase I

A carbohydrate binding protein was found in mid-lactating rat mammary gland. This rat mammary gland lectin agglutinated trypsinized rabbit erythrocytes and the hemagglutination was inhibited by the addition of β-d-galactosides such as lactose, melibiose, UDP-galactose and thio-d-galactoside. The lectin was partially purified by affinity chromatography on a column of Sepharose 4B to which asialo-fetuin had been covalently linked. Rat mammary gland lectin is a glycoprotein with a molecular weight of 14,800, estimated from SDS-PAGE, or 16,800 from gel filtration.

The occurrence of two glycoproteins, C4-casein and α-lactalbumin, is known in rat milk. Bovine κ-casein is a well-characterized glycoprotein. These glycoproteins were found to be bound by the rat mammary gland lectin, when they were desialylated by the action of neuraminidase. Neuraminidase-untreated α-lactalbumin also bound to the lectin but to a lesser extent. The level of the lectin in rat mammary gland was greatly reduced during regression of the gland after weaning.     

Purification and Some Properties of Active β-Amylase in Rice

An active β-amylase was purified from germinated rice seeds by precipitation with ammonium sulfate, acid treatment, chromatographies on DEAE-cellulose and DEAE-Sephadex A-50, and gel filiations on Sephadex G-75. The purified enzyme was homogeneous in disc electrophoretic analysis.

The molecular weight was estimated to be approximately 53,000 by thin-layer gel filtration and polyacrylamide gel electrophoresis. The isoelectric point was found to be pH 5.0 by disc electrofocusing.

The optimum pH was found to be in the pH range of 5.5 to 6.5. The Km value for soluble starch was 3 mg/ml. The enzyme was inhibited by sulfhydryl reagents or heavy metal ions.

The active β-amylase was oxidatively dimerized by treatment with 0.3 m ferricyanide in 3 m urea. The dimerized enzyme was thought to be one of inert β-amylases in ungerminated rice seeds.