IUCrJ (2020). 7, 985–994] advance our understanding of the catalytic mechanism of carbonic anhydrase II by studying a mutant V143I where the change (of one hydrophobic amino acid to another that differs by a single CH2 group) is probably the smallest alteration that can be introduced into a protein. The study was performed at high pressure in a CO2 atmosphere to visualize the bound substrate; it showed the behavior of the entrance conduit waters and the substrate alteration due to the mutation.Kim and co-workers [ It has been especially widely used as a biophysical model for protein stability and binding studies  that can be introduced into a protein with the hope that such a change would cause almost no deviation in the catalytic properties of the enzyme.Writing in this issue of  al. 2020 advance kcat value was not significantly affected, but the affinity of the substrate CO2 for CA II decreased tenfold, from 10 mmM K for CA II to 100 mmM K in the mutant. The study by Kim et al.  water molecules. It would be very interesting to see the positions of each proton in the conduit, but this would require neutron crystallography (Fisher


These results were then compared with those of the current partition boards in the market. It was noted that the composite was suitable for use in partition boards. Again, regarding cost, these composites are cheaper to produce at a rate of $11.33/m2 compared to the timber costing at $16/m2.Composites are gradually becoming an integral material for structural and manufacturing processes. Sisal fibre has the potential to be one of the leading reinforcement natural fibres, not only in Zimbabwe, but the world over for use in glass composites. This is mainly due to it being inexpensive, exhibiting a low density, high specific strength, a high stiffness to weight ratio, non-toxicity, its abundance in Zimbabwe and its renewability. With an aim of coming up with a composite for partition board applications in the construction industry, five samples of sisal-glass composite were prepared with varying sisal fibre content and different layering techniques. Sisal fibres were pre-treated with 20% NaOH to enhance their crosslinking with the resin and reduce the chemical impurities such as lignin and waxes. Mechanical  and physical tests (density and water absorption) were conducted to analyse the properties of the composite. The results showed that treated sisal fibres had a higher breaking tenacity of 33.11 g/tex which is higher than untreated fibres with a breaking tenacity of 25.72 g/tex. The best properties were obtained in the sample with 4 layers of glass and 9 layers of sisal fibres using 200 ml of resin. The composite had a tensile strength of 57.60 MPa, flexural strength of 36 N/mm Glass reinforced fibre composite materials give the engineer an alternative of performance of materials for the automobile sector and building sector. The increased demand of natural fibre is due the fibres being inexpensive, low density, environmental friendliness, inexhaustible supply and its profusion6. Due to the lightweight nature of natural fibres and the mechanical properties of natural fibre composites the number of application in the modern industry has increased and even includes transportation and aerospace industries4.The advancements in the field of textiles led to many new and advanced materials. These gave rise to the development of composites materials. Composite materials, in simple terms, are materials that are comprised of at least two different materials with one the materials remaining in a solid state during fabrication6. While natural fibres traditionally have reinforced thermoplastic polymers, they have attracted greater attention in reinforcing glass fibres with their added advantage of mechanical properties which are required in automobile applications7. Use of sisal fibres to reinforce glass has several advantages such as reduced environmental impact from synthetic fibres and decomposability of components9. The most commonly used fibres are called bast fibres or skin fibres. They are located in the phloem of specific plants, mainly dicotyledonous plants. They provide the support to conductive cells and the necessary rigidity required by the stem6. Fibres are separated from the plant using a process known as retting. Generally, bast fibres exhibit higher strength when compared to other natural fibres hence their use in high quality textiles. Types of bast fibres found in nature include flax, hemp, jute and kenaf10. Bast fibres have been used to manufacture traditional textiles such as carpets, hessian or burlap, paper and sacks.Sisal fibres are a renewable resource, and the production of this fibre uses a small amount of energy5. Short conventional fibres such as aramid, glass and carbon have recently found increasing number of products as reinforcement for polymeric materials. The objective of the use of short fibres is to improve the mechanical properties of polymeric material while keeping costs low11.In more advanced textile materials they have been used to manufacture nonwovens and composite materials that have been used to make products such as mats, furniture materials, door panels for vehicles and carpets12. Thermosets have the advantage of the ability to be cured to a high degree that results in a 3D network structure that is tough, resists creep and solvents. Due to fibre alignment the loading can be above 70%13.The capability of the composite is dependent on the matrix as the matrix is responsible for the distribution of the loads on the composite material. Composites are made of a matrix and a reinforcement component. The matrix is important in the performance of the composites. Due to their ease of handling and good bonding between the matrix and fibres, thermosets have been used for the matrix component14. Dispersion of fibres in thermoplastic composites plays an important part in efficient and even distribution of fibres in composite materials8. Although the processability of the composite materials results in ion limited loading, thermoplastics composites exhibit good properties such as desirable toughness and mechanical properties. Fibres in the composite have been found to be of a randomly oriented nature leading to reduced property medication of thermoplastic composites. The resulting properties of the composite material will be determined by the fibre, fibre matrix interface and the aspect ratio of the fibre8.Most of the work has centred on synthetics such as polyethylene, poly vinyl alcohol, polypropylene and polystyrene as they can be easily processed at temperatures below 200 °C, above which natural fibres would be damagedBonding between the fibre and the matrix is essential in determining the forces that the matrix can withstand. Strong bonding between matrix and fibres means that there will be a high level of force transfer as it moves from the matrix to the fibre, thereby allowing the fibres to effectively perform the load bearing function.14. During processing at high temperatures, the walls of the sisal fibre undergo pyrolysis which results in formation of charred layers. These charred layers will then form a protective layer around the lignocellulose to prevent further degradation. Thus the key factors in the fabrication of sisal reinforced composites are thermal stability of the fibre, bonding behaviour of sisal, and distribution of the fibre in the composites15.The thermal stability of the sisal fibre is critical in the manufacture of composite materials as high temperatures are experienced during the curing stage of the thermosetsThe composite fabrication process was fabricated using the hand lay-up method. Unsaturated orthopthalic polyester resin was used as the polymer for the matrix. The resin with supplied with an accelerator, that is, cobalt napthanate already mixed so the only additive that was added to the resin was the catalyst, Methyl ethyl ketone peroxide (MEKP). Wax was used as the release agent. A designed 400 × 400 mm metal weight (10 kg) was used for the compression of the composite. Plastic brushes and a roller were used for the spreading of resin and removing air pockets respectively.9. The Sisal fibres were treated to reduce the water absorbency of sisal fibres and to remove impurities and enhance the surface reactivity of sisal fibres.Natural fibres are generally extracted with some percentage of impurities and they contain cellulose and lignin which may prevent them from reacting with other chemicals. In order to remove this and increase the surface reactivity of sisal for cross linkage with the resin, pre-treatment with a NaOH was done in a process known as mercerisationThe fibres were treated by immersing 200 g of sisal fibres in 20% sodium hydroxide for 1hour at room temperature. The fibres were then rinsed with distilled water then dried in open air for 2 days. Treated fibres were also characterised for their strength properties using an Instron tensometer with a load cell of 3 kN. Obtained values from the test were recorded, averaged and compared to the tensile properties of the untreated fibres.Treated and untreated sisal fibres were chopped into short lengths of 50 mm. The sisal fibres were randomly laid to produce mats of dimensions 300 mm × 300 mm. Each sisal mat had a mass of 10 g and each glass mat had a mass of 66 g. Each composite specimen consisted of totally 4 layers of glass mats and alternating layers of sisal fibres for the preparation of different samples, as shown in Table Glass material which was used was E-glass, woven roving with a density of 450 gsm.The glass fabrics were cut into 5 weft and 5 warp direction specimens according to the dimensions 150 mm × 100 mm as described in the ASTM D5034 standard for grab test. The specimen were then tested for their individual tensile properties using a Titan universal strength tester with a load cell of 3 kN at a rate of 100 mm/s. Load was applied to the test specimen until it broke and values for maximum load and elongation were recorded. The Glass fibres matts were obtained from ACOL Chemicals, Bulawayo.The matrix used to fabricate the fibre material was preaccelerated orthophthalic polyester with a density of 1.10 g/cc. Curing of the resin was initiated by a catalyst, methyl ethyl ketone peroxide (MEKP) which hardens the fibre composite without external heat. Curing time of the polyester resin was 18minutes from the time it was mixed with the hardener. To prevent hardening before finishing the fabrication process, mixing of the resin was done, using a steel rod, prior to the fabrication. A measured amount of polyester resin was taken for different fibre content and mixed with the hardener in the ratio of 1% w/v. The solution was mixed and stirred before applying on the laminate. The resin used was sourced from ACOL Chemicals, Bulawayo which supplied the preaccelerated orthophthalic polyester from the NCS Resin Company.A mixture of polyester resin and catalyst was hand laid on polyethylene film covered with a release film to prevent sticking. In this case, a thin layer of wax was used as the release film. The amounts of resin and catalyst used are shown in Table Finally, these samples were left for 24 h for them to cure at room temperature (24 °C) to get the desired samples. After the composite material cured, the composite material was then taken out and post curing was carried out in an oven at 50 °C for 4 h. A laboratory type oven was used for post curing of the composite material.This test method determines the in-plane tensile properties of polymer matrix composite materials. Tests were carried according to ASTM D3039 standard using an Instron Universal Testing Machine (UTM) at room temperature conditions and at a speed of 2 mm/min. 5 specimen were tested, each being averaged from the 5 tests. Specimens used for testing were 250 mm length by width 25 mm.Ftu = ultimate tensile strength, MPa; Pmax = maximum load before failure, N; A = Average cross-sectional area (W × H), mm2; σ = Ultimate tensile stress; E = Elastic modulus; The formulae that were used to calculate the values of tensile strength, strain, elongation and modulus of elasticity are given below:The standard used for flexural test was ASTM D790. A DMG Universal Testing Machine was used to carry out the Flexural test. Flexural test determines the maximum stress induced in the outermost fibre.max = maximum force (N); L = length (100 mm); H = thickness and B = breadthThe flexural strength is calculated from the maximum load of the composite using the following formulas.ASTM 5229- Is the Standard Test Method that is used for composite materials to determine the Moisture Absorption Properties and Equilibrium Conditioning. Five tests were carried out per sample. The tests for determining moisture absorption were carried out at room temperature.Percentage water absorption is then calculated from:The hardness test was carried out following the procedures of the ASTM F1957 standard. The test characterizes the indentation hardness of materials through the depth of penetration of an indenter. The test is mostly used for composite materials to determine the curing degree of reinforced thermosets. There test also determines the resistance to plastic deformation by a material..The flammability test was carried according to ASTM D635 standard. The flammability test carried out to determine the rate at which a material will burn when placed near a flameTo obtain the properties of the fabricated composite, five samples of different fibre and matrix mass percentages were prepared. The respective mass percentages were obtained from the weighed masses of the composite using a digital analytical scale. The compositions of the composite samples were calculated using the formulas in the methodology section. From each of the samples, three test specimen were obtained and subjected to bending, tensile, hardness and moisture absorption tests. The results of the fabrication of the composite are shown in Table Tensile test results show how much force the composite can withstand without breaking and from the load-elongation graph the tearing and deformation properties can be obtained. Table Table 16. Again, with the medium strength cement board, the least strong composite can withstand a maximum force of 57.6 MPa which is almost nine times more than the fibre cement load of 6.99 MPa and two times that of the HDPE partition boards which have a tensile strength of 27.6 MPa17.Comparing the tensile properties of current partition boards and the fabricated composite board, it can be clearly noted that the composite is stronger in tensile strength compared to soft wood boards which have a tensile strength of 23 MPaFigure As shown in Fig. It is noted from Figs. As shown in Fig. Using sample 1, the tabulated results in Table 2 and its deflection at break is 5.0 mm. this is because it is more brittle compared to the other specimens hence it quickly reaches the maximum load without too much deflection.Flexural stress of the composite with the highest flexural strength is 156 N/mm2 which is higher than that of untreated composites.As shown in Fig. The interfacial zone between two surfaces in a composite material, namely between the fibres and the matrix plays an important part in the performance of the composite. Load transfer will depend on the interfacial zone between the fibre and the matrix thereby affecting strength characteristics of the composite. On the other hand, flexural failure will be determined by the bonding between the fibre and the matrix. Increase in the flexural strength can be attributed to the increase in effective surface area available to the matrix to bond. Flexural strength decreases with increase in sisal fibre content shown by the decrease in the graph size from sample 1 to 5. Due to the random orientation of the sisal fibres, more layers will cause more weak areas which are not uniform in their bonding strength, hence there is presence of more weak spots in the composite. Deformations can occur at lower loads than a composite with low sisal fibre content.18. Compared to that of wood partitions the composite has also higher flexural properties than wood, which is 32 N/mm217.A composite with alternating layers of glass and sisal, that is comparing sample 1 and 3, has a higher flexural strength than that which has sisal fibres concentrated in the middle portions of the composite (sample 3). The difference in strength of the two fibres used will cause the composite to be stronger in the outer layers than the core area. Hence when a bending force is applied the core is likely to start deforming and cause the outer layers to break also. The sample with the lowest flexural strength, that is, sample 5 with a maximum bending load of 490 N has greater flexural properties than that of the current gypsum board which has the same thickness, that is, 489N19. Mercerisation also improves absorption of chemicals by the fibre by opening the lumen of the sisal fibres thereby allowing exposure of reactive sites. The opening of the reactive sites allows crosslinking of the resin and the fibre leading to better bonding, hence an increase in the strength of the composite material.Tests carried out for breaking tenacity showed that there is a higher breaking tenacity for the treated fibres as compared with the untreated fibres. Samples with treated fibres showed an average breaking tenacity of 33.11 g/tex while samples with untreated fibres had an average of 25.72 g/tex. Treatment with NaOH (mercerisation), allows for even distribution strength properties along the fibre length as it increases the lowest strength that the fibre can withstand at any point along its lengthTable The trend line in Fig. 16. Gypsum boards of 6 mm, which is close to sample 1, have a moisture percentage of 10% yet the composite is only 4.5%18. This shows that the composite has good properties than the currently used boards in terms of their water absorption20.The composite has an average water absorption percentage which is far less than that of sisal fibres and that of glass, separately. This is due to the use of the polyester resin which also absorbs little water. The composite absorbs less moisture compared to wood partition boards which has a moisture absorption of 12% which is also 2% less than the highest absorption value of the compositeThese tests determine the resistance of the composite to deformation caused by low impact forces such as indentations.From Fig. 16. It is a soft material when comparing its hardness properties to composite. It is easily scratched and leaves marking of low force indentions.The composite material is considered a hard material according to the classes of the ASTM standard F1957. Wood partitions are generally made from softwood, such as Pine woodThe behaviour of burning of the composite at various fibre layers was observed according to ASTM D 635-76 standard. The results of duration of burning are given in Table The composite burnt with an orange flame producing a black soot with a chocking polyester/plastic smellThe composite did not easily catch fire as it took 6 s but burnt easily after catching fire.The remains were black in colour with hard black particles as shown in Fig. The following observations were made during the burning:Wood partitions are prone to fire hazards. They quickly catch fire and burn readily leaving white ashes. With the composite, it is slow at catching fire and the burning characteristics shows that it takes longer for the fire to spread.2 respectively. This translates to 239% Also comparing the water absorption properties, the composite has a value of 10% on average which is 2% less that of wood. Mercerisation of sisal fibres results in changes in the structure of sisal fibres which lead to improvements as results show that the composite with treated fibres have higher mechanical properties though there is no difference in the burning properties. The removal of surface impurities on plant fibres may be an advantage for fibre to matrix adhesion as it may facilitate both mechanical interlocking and the bonding reaction due to the exposure of the hydroxyl groups to chemicals such as resins and dyes. However, sisal untreated fibres also show higher mechanical properties compared to wood, hence they can be used untreated to produce the composite without additional costs.The current and future application of sisal fibre in composite materials, specifically polymer based composites were explored. The low density and high specific properties of the composite material fabricated in the article, the composite material may find use in vehicle interiors. A sisal-glass composite was successfully developed. With the sample with 4 layers of glass mats and 9layers of sisal being the one preferred since it has the overall thickness which is almost that of the standard partitioning board, that is, 10 mm. Comparing the mechanical properties of the current timber boards in use, the composite has tensile strength and flexural strength which is greater by 32.2 MPa and 4.63 N/mm


We study the dynamical Casimir–Polder force on a two-level atom with different initial states in the one-dimensional dielectric cavity with output coupling, and obtain the analytical expression of the expectation value of dynamical Casimir–Polder force. Results show that the expectation values of dynamical Casimir–Polder force may be affected by the initial states of the atom. Moreover, the expectation value of Casimir–Polder force may vanish at some special atomic positions by properly selecting the initial state of the system. The effects of different relative dielectric constants and the cavity size on the expectation value of Casimir–Polder force are also discussed. The Casimir–Polder force is the long-range interaction between neutral polarizable particles and macroscopic objects, which is successfully observed in experiments3 and has received considerable attentions7 due to its potential application in basic physics and designing of quantum devices11. For instance, the Casimir–Polder force has covered various configurations such as trapping cold atoms near surfaces16, quantum reflection20, graphene21, Bose-Einstein condensates22, and carbon nanotubes25. At finite temperatures, the key correlations between these interactions and the topological and magnetoelectric properties of interacting objects are highlighted27.The existence of electromagnetic vacuum fluctuations has caused many quantum effects, such as Casimir–Polder force31. In Ref.31 and4, the authors investigate the dynamcial Casimir–Polder force on a two level atom placed before a ideal metal plate starting from the ground state and the excited state, respectively. Results show that the static Casimir–Polder force is always attractive for the ground state condition, while the static Casimir–Polder force is either attractive or repulsive for the excited condition. At some special atom-wall distances, the static Casimir–Polder force can vanish for the excited condition. Besides, the static Casimir–Polder force for the excited-atom condition is much greater than that of the ground-atom condition.In recent years, many studies have focused on the Casimir–Polder force on atoms when they start from ground or excited states32. The analytical expression of the expectation value of dynamical Casimir–Polder force on atom starting from the superposition state will be obtained. And the effects of the dielectric and cavity size will be discussed.In this paper, we will investigate the effect of initial states on the dynamical Casimir–Polder force acting on a two-level atom in a caivity with output couplingThe paper is organized as follows: in “34We consider a one-dimensional cavity which is filled with a dielectric (Region 1 shown in Fig. We can defined the parameter j in Eq. 4\documen35, the equations of 31.t=0, and the probability of being in the ground state is 32, and obtain the expectation value of Casimir–Polder force by Eq. (In the above section, we have analyzed the electromagnetic field in the cavity and the Hamiltonian for the system. In the following, we will calculate the expectation value of dynamical Casimir–Polder force In this section, we focus on finding the characters of the expectation value of Casimir–Polder force on an initially general superposition state atom. Figure entclass1pt{minimaentclass1pt{minimaTo further investigate the effect of the relative dielectric constant and the cavity length on the expectation value of Casimir–Polder force, we describe the plots of the Casimir–Polder force with different values of the relative dielectric constant and the size of cavity versus Figure In this paper, we have calculated the expectation value of dynamical Casimir–Polder force on a two-level atom starting from the different initial states in the one-dimensional cavity with output coupling by using the perturbation theory, and have obtained the analytical expression of the expectation value of dynamical Casimir–Polder force.We have observed the relationship between the expectation value of Casimir–Polder force and the initial state of the atom, that is, the expectation values of dynamical Casimir–Polder force may be affected by the initial state of the atom. By selecting the proper initial state, the expectation value of Casimir–Polder force may vanish at some special atomic positions. The relative permittivity and the size of the cavity may also affect the expectation value of the Casimir–Polder force.


Although previous studies have analyzed active and passive tactile processing in humans, little is known about the neurophysiological basis of width discrimination in humans. Here we present a width discrimination task for humans that reproduces the main features of the width discrimination task previously developed for rodents. The task required subjects to actively or passively sample two movable bars forming a “narrow” or “wide” aperture. Subjects were then required to press one of two buttons to indicate if the bar width was “narrow” or “wide”. Behavioral testing showed that subjects were capable of discriminating between wide or narrow apertures up to distances of 0.1 cm. Electroencephalography (EEG) recordings further suggested distinct topographic maps for active and passive versions of the task during the period associated with the aperture discrimination. These results indicate that the Human Differential Width Discrimination Task is a valuable tool to describe the behavioral characteristics and neurophysiological basis of tactile processing. A) The customized Graphical User Interface (GUI) allows the experimenter control over a variety of system management functions, such as, running different versions of the width discrimination task, configuring setups, visualizing finger and saving behavioral data. (B) The Behavioral Apparatus is where the subjects perform the task. It is composed of a box with a small opening that allows the subject to insert the index finger and perform the width discrimination. Inside the small opening is a variable-aperture width determined by two bars. This variable-aperture width is set by a programmable integrated circuit using an Arduino microcontroller. The frontal panel has a central light, two push buttons on the top right corner, and a four-digit LED display on the left corner. C) Steps of Behavioral Protocol: at the start of each trial, the center light is red and the bars are set to a predetermined position, while the subject keeps the finger out of the behavioral apparatus (1). When the center light turns yellow, the subject must insert the finger in the opening and wait for the algorithm to detect the correct finger positioning (2) as the finger reaches the aperture width, the center light turns green. At this point, if the subject is performing the active version of the task, the subject is allowed to move the finger in order to sample the aperture width. Otherwise, if the subject is performing the passive version of the task, the subject is required to remain with the finger in place, and the bars will move towards the finger stopping at the specified aperture width. In active and passive versions the aperture width can be “narrow” or “wide”. This is the Discrimination period (3). After tactile sampling, the light will turn red and the subject is required to remove the finger from the aperture width (4), after which the subject must press one of two buttons, to indicate if the aperture width was narrow (and the subject should press the Blue button) or wide (and the subject should press the Yellow button) (5).Representational diagram of the behavioral width discrimination task for active and passive tactile discrimination in humans. ( Specifications TableThe width discrimination task apparatus hardware is presented in The apparatus box has the dimensions size of 40 × 35 × 20 cm , with an opening located in the center of the frontal panel. This opening is where the subject's finger is inserted.  An additional hand cover with 10 × 10 × 10 cm .The electromechanical part consists of two Nema 17-size stepper motors controlled by an Arduino Mega 2560 and a Dual Bipolar Stepper Motor Shield for Arduino  stepper driver. This circuit allows the control software to operate the stepper motors in linear motion and thus control the aperture width. The aperture is set through the movement of two stimulation bars 40 × 20 mm , built using a 3D printer Polylactic acid plastic. The position of the bars related to the subject's finger is limited in order to prevent any discomfort or injury. This is implemented by limiting the current that can be supplied to the motors. Also, the bars are built using honeycomb infill pattern of 30% density, thus, they will break if too much force is exerted against the finger. Bending will only occur if the bars are set to move beyond the distances required for testing. These two bars are located at either side of the Center-Finger Place  to indicate the subject which action must be performed in each epoch of the trial . These buttons are high-quality arcade-style pushbuttons , delivering consistent and false-positive safe responses. Finally, on the left top corner of the frontal panel, a four-digit led display  can be used to transmit relevant information such as the trial number or the outcome of each trial.https://github.com/avperrotta/tactileFeedback) and is mouse-navigated and controlled. An example of the software is shown in The custom control software was implemented using Max programming language and was designed to provide a Graphical User Interface (GUI), where the experiment manager can calibrate the mechanics, algorithms, and experiment parameters. This software is available on Github  when the center light turned yellow, indicating that the subject had to place the finger in the CFP. Then, if the algorithm detected that the finger was in the correct position, the trial light turned green. At this point the subject was allowed to sample the aperture width for a period of time . Sampling the aperture width could be active or passive. If the block was active, the subject could move the finger to explore the aperture width. Otherwise, if the block was passive, the subject was required to remain with the finger in the CFP, and the bars would move towards the finger. After the discrimination, the center light turned red, and the subject was required to remove the finger from the frontal panel opening and press one of the two push buttons to indicate the aperture width of that particular trial. After a button was pressed, the four LED digital display would indicate if the response was correct or incorrect and the total number of correct responses was updated. This corresponded to the end of the trial. An intertrial interval of 500±50 ms (Mean±SEM) would then be signaled with the center light turning again red. This indicated that the subject should not introduce the finger in the frontal panel opening.Before the EEG cap was placed, the subject was allowed a short practice session  in which the subject was familiarized with the specific instructions to each tactile discrimination condition.The present research protocol was approved by the Ethics Committee of the Universidade do Minho , according to the Code of Ethics of the World Medical Association (Declaration of Helsinki) for experiments involving humans. All participating subjects voluntarily filled an informed consent.n = 9) that were tested with the goal of exploring the upper and lower bounds of the task, namely: number of trials, intertrial intervals, session duration, trial duration, active and passive discrimination limits, effects of different versions of the task in EEG recordings.Due to the methodological nature of the present manuscript we will only present results from a reduced number of subjects . However, in active discrimination, the subject can be tested in apertures that are larger than the width of the finger.X axis of Having this in mind, we then tested the subject in the active version of the task, but now using a different reference for the “narrow” distance. The subject was now required to explore two different apertures, but both were larger than the subject's finger. The narrow aperture was now 0.5 cm larger than the finger on both sides (an excess of 1.0 cm), and the wide aperture was varied according to the .Nine subjects were then tested in active and passive versions of the task according to the behavioral protocol described in Having demonstrated that subjects could present a variety of behavioral performances when tested in active or passive versions of the task, we then asked if the neurophysiological basis of each version would also be different. For this, we recorded EEG data from seven subjects performing the task in active and passive versions according to the conditions described in the behavioral protocol .


Modern machine learning is based on powerful algorithms running on digital computing platforms and there is great interest in accelerating the learning process and making it more energy efficient. In this paper we present a fully autonomous probabilistic circuit for fast and efficient learning that makes no use of digital computing. Specifically we use SPICE simulations to demonstrate a clockless autonomous circuit where the required synaptic weights are read out in the form of analog voltages. This allows us to demonstrate a circuit that can be built with existing technology to emulate the Boltzmann machine learning algorithm based on gradient optimization of the maximum likelihood function. Such autonomous circuits could be particularly of interest as standalone learning devices in the context of mobile and edge computing. Ii to the ith BSN from the outputs mj of all other BSNs.Machine learning, inference, and many other emerging applications  is linear and is defined by a set of weights Wij such thatS is the time it takes to recompute the inputs {I} everytime the outputs {m} change. Typically Equations (1), (2) are implemented in software, often with special accelerators for the synaptic function using GPU/TPUs  and (2) are implemented on a digital computer using a clock to ensure that neurons are updated sequentially and the synapse is updated between any two updates. But they play an important role in clockless operation of autonomous hardware that makes use of the natural physics of specific systems to implement Equations (1) and (2) approximately. A key advantage of using BSNs is that Equation (1) can be implemented compactly using stochastic magnetic tunnel junctions (MTJs) as shown in Camsari et al.  and (2) can be used for inference whereby the weights In this paper we present a new approach to the problem of fast and efficient learning that makes no use of digital computing at all. Instead it makes use of the natural physics of a fully autonomous probabilistic circuit composed of standard electronic components like resistors, capacitors, and transistors along with stochastic MTJs.vivj is the correlation between the ith and the jth entry of the training vector {v}n. The term mimj corresponds to the sampled correlation taken from the model's distribution. The advantage of this network topology is that the learning rule is local since it only requires information of the two neurons i and j connected by weight Wij. In addition, the learning rule can tolerate stochasticity for example in the form of sampling noise which makes it an attractive algorithm to use for hardware machine learning , a form of stochastic recurrent neural network, for which the most common learning algorithm is based on the gradient ascent approach to optimize the maximum likelihood function Wij with the voltage on a capacitor C driven by a voltage source  with a series resistance R  and mimj = Vm,ij/(VDD/2). From Wij = AvVij/V0, λ = V0/(AvVDD/2), and τL = λRC where Av is the voltage gain of OP3 in V0 is the reference voltage of the BSN. For proper operation the learning time scale τL has to be much larger than the neuron time τN to be able to collect enough statistics throughout the learning process.For our autonomous operation we replace the equation above with its continuous time version (τstance R :(4)CdViW with voltages rather than with programmable resistances for which memristors and other technologies are still in development –(3). The training images or patterns {Vv,ij which is given by the ith entry of the bipolar training vector vi multiplied by the jth entry of the training vector vj and scaled by the supply voltage VDD/2. The training vectors can be fed in sequentially or as an average of all training vectors. The weight voltage Vij across capacitor C follows Equation (4) where Vv,ij is compared to voltage Vm,ij which represents correlation of the outputs of BSNs mi and mj. Voltage Vm,ij is computed in the circuit by using an XNOR gate that is connected to the output of BSN i and BSN j. The synapse in the center of the circuit connects weight voltages to neurons according to Equation (2). Voltage Vij has to be multiplied by 1 or −1 depending on the current value of mj. This is accomplished by using a switch which connects either the positive or the negative node of Vij to the operational amplifiers OP1 and OP2. Here, OP1 accumulates all negative contributions and OP2 accumulates all positive contributions of the synaptic function. The differential amplifier OP3 takes the difference between the output voltages of OP2 and OP1 and amplifies the voltage by amplification factor Av. This voltage conversion is used to control the voltage level of Vij in relation to the input voltage of each BSN. The voltage level at the input of the BSN is fixed by the reference voltage of the BSN which is V0. However, the voltage level of Vij can be adjusted and utilized to adjust the regularization parameter λ in the learning rule (Equation 3). The functionality of the BSN is described by Equation (1) where the dimensionless input is given by Ii(t) = Vi,in(t)/V0. This relates the voltage Vij to the dimensionless weight by Wij = AvVij/V0. The hardware implementation of the BSN uses a stochastic MTJ in series with a transistor as presented by Camsari et al. (The autonomous learning circuit has three parts where each part represents one of the three Equations (1)–(3). On the left hand side of i et al. . Due to i et al. .N. For every entry of the training vector one BSN is needed. The number of weights which is the number of RC-circuits is given by N(N − 1)/2 where every connection between BSNs is assumed to be reciprocal. To learn biases another N RC-circuits are needed.Vm,ij) and compares it to the target distribution  without reinitializing the Markov Chain after a weight update. During the learning process voltage Vij reaches a constant average value where Vij = Vij,learned corresponds to the learned weight.The learning process is captured by Equations (3) and (4). The whole learning process has similarity with the software implementation of persistent contrastive divergence (PCD) 2πl. For the transistors, 14 nm HP-FinFET Predictive Technology Models (PTM)fin = 1 for the inverters and fin = 2 for XNOR-gates. Ideal operational amplifiers and switches are used in the synapse. The characteristic time of the BSNs τN is in the order of 100 ps ) is plotted as a function of training time t in PSPICE(t) is the normalized histogram taken over 500 ns. PSPICE at t = 0 corresponds to the histogram taken from t = 0 to t = 500 ns. During training the KL divergence decreases over time until it reaches a constant value at about 0.1. It has to be noted that after the weight matrix is learned correctly for a fully visible Boltzmann machine, the KL divergence can be reduced further by increasing all weights uniformly by a factor I0 which corresponds to inverse temperature of the Boltzmann machine  to a clockless autonomous circuit. We have shown full SPICE simulations to demonstrate the feasibility of this circuit running without any digital component with the learning parameters set by circuit parameters. Due to the fast BSN operation, samples are drawn at subnanosecond speeds leading to fast learning, as such the learning speed should be at least multiple orders of magnitudes faster compared to other computing platforms (Adachi and Henderson, The datasets generated for this study are available on request to the corresponding author.JK and SD wrote the paper. JK performed the simulations. RF helped setting up the simulations. KC developed the simulation modules for the BSN. All authors discussed the results and helped refine the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The authors would like to make a correction to their published paper .The authors would like to change one incorrect sentence in .On page 3, in paragraph one, the sentence: “A difference in net charge on the molecule guides choice of ligand with higher affinity; LDHA with a negative 6 (−6) net charge has higher affinity for pyruvate as compared to a net positive charge (+1) of LDHB which has higher affinity for lactate [24].” should be changed to “A difference in net charge on the molecule guides the choice of the ligand with higher affinity; LDHB with a negative 6 (−6) net charge has a higher affinity for lactate as compared to a net positive charge (+1) of LDHA, which has a higher affinity for pyruvate [24].”The change does not affect the scientific results.The rest of the manuscript does not need to be changed. The authors would like to apologize for any inconvenience caused. The manuscript will be updated, and the original will remain available on the article webpage.


Delta check is a patient‐based QC tool for detecting errors by comparing current and previous test results of patient. Reference change value (RCV) is adopted in guidelines as method for delta check, but the performance is not verified. We applied RCV‐based delta check method to patients' data and modified for application.Reference change value were calculated using results of internal QC materials and biological variation data. Test results of 17 analytes in inpatients, outpatients, and health examination recipients were collected. The detection rates of currently used delta check method and those of RCV‐based method were compared, and the methods were modified.Reference change value‐based method had higher detection rates compared to conventional method. Applied modifications reduced detection rates. Removing the pairs of results within reference interval reduced detection rates (0.42% ~ 10.92%). When RCV was divided by time interval, the detection rates were similar to prior rates in outpatients (0.19% ~ 1.34%). Using RCV multiplied by twice the upper limit of reference value as cutoff reduced the detection rate (0.07% ~ 1.58%).Reference change value is a robust criterion for delta check and included in clinical laboratory practice guideline. However, RCV‐based method generates high detection rates which increase workload. It needs modification for use in clinical laboratories. Reference change value (RCV) can be used for determination of delta check limit. However, this study revealed that simple adaptation of RCV for delta check limit can produce excessive detection and increase the laboratory's workload. Further consideration and modification are needed for RCV and delta check before actually applying them to the clinical laboratory. Delta difference is defined as the absolute difference between current results and previous results. Delta percent change is defined as the percentage of the difference between the current results and the previous results divided by the previous one. Rate difference is defined as the delta difference divided by the interval between the current and past testing time, and the rate percent change is defined as the delta percent change divided by the interval between the current and the previous testing time. The selection of the delta check method depends on the extent of the test value, the distribution of the test value, etc.There are two methods to determine the limit of delta check, that is, using biological variation and using patient data. CLSI EP33 “Use of Delta Checks in the Medical Laboratory” provides a detailed description of how to use the Reference Change Value (RCV) as a method of delta check limit determination using biological variation. RCV is the minimum difference of a measurement from a reference value that is considered as distinguishable from measurement uncertainty,22.1We collected results of internal quality control materials  over 1 year (2016) to calculate RCV. During 2016, QC materials with the same lot number were used. Test results of inpatients, outpatients, and general health examination recipients were collected over a 4‐week period . The patients' previous test results within 60 days for outpatients and 40 days for inpatients were also collected and paired. Test results of the following analytes were collected: calcium (CA); glucose (GLU); creatinine (CREA); uric acid (UA); cholesterol (CHOL); protein (PROT); albumin (ALB); aspartate aminotransferase (AST); alanine aminotransferase (ALT); alkaline phosphatase (ALP); total bilirubin (TBIL); phosphorus (PHO); blood urea nitrogen (BUN); direct bilirubin (DBIL); sodium (NA); potassium (K); and chloride (CL).Patient specimens and QC materials were tested using automated chemistry analyzers. A dedicated calibrator and a dedicated reagent provided by the manufacturer of each type of equipment were used. Roche Cobas C‐8000 (Roche diagnostics) was used for inpatient and health screening recipient samples, and Beckman Coulter AU5800 (Beckman Coulter) was used for outpatient samples.2.2Based on CLSI EP29‐A, the standard deviation of the inspection values of the internal quality control material for 1 year was used to calculate the uncertainty using the top‐down approach.k, two types of Confidence interval (CI) (95% and 99%) were used to calculate RCV (k = 1.96 for the 95% confidence level and k = 2.58 for the 99% confidence level).For coverage factor 2.3RCV with 95% CI and RCV with 99% CI were used to compare the percent difference between the previous test and the current test results and used as the cutoff value of the delta percent change. For each analyte, the detection rate of the current delta check criteria2.4In the delta check rule setting process using RCV, some modification was performed in order to reduce the excessive detection rate. First, we excluded pairs of test results if both test results constituting the pair were within the reference interval of the analyte. The detection rate at this time was compared with the detection rate before the modification. Second, for analytes whose current delta check rule uses the rate difference and rate percentage change, the percent change was compared with the RCV with a 99% CI value divided by the interval of the test date, and the detection rate was calculated and compared with the detection rate before modification. As a third method, in the case of an analyte whose current delta check method uses the delta difference and rate difference, RCV with a 99% CI value multiplied by twice the upper normal limit value of the reference range was compared with the absolute difference of each pair and the detection rate was calculated and compared with those before the modification.33.1Overall, 491 699 pairs of test results from inpatients, 332 840 pairs of test results from outpatients, and 32 100 pairs of test results from general health examination recipients were collected. A total of 66 842 test results of quality control materials were used to calculate RCV (Table 3.2Reference change value was calculated using CVa and CVi, and RCV with a 99% CI showed a significant difference from 4.4% to 136% depending on the analyte (Table 3.3In terms of the detection rate, both methods using 95% CI and 99% CI tend to have a higher detection rate compared to those using traditional cutoff Table . This tr3.4We tried adjusting RCV‐based delta check methods in several ways to find a way to mitigate over‐detection. First, we detected pairs exceeding the RCV with a 99% CI value, except when both of the results constituting the pair were within the reference interval Table . In thisSecond, if the existing delta check method has a rate difference or rate percent change, the RCV is compared with the rate percent change to reflect the time factor for the analyte Figure . ApplyinAs the third modification method, when the current delta check method uses a delta difference or rate difference, the value obtained by multiplying the RCV value by twice the upper limit of the reference value was used as the delta check cutoff. As a result of this method, all of the analytes except for glucose showed much lower detection rate of less than 2% in both the inpatient and outpatient groups (Figure 4The concept of the delta check was originally proposed by Nosanchuk and GottmanThe concept of uncertainty has been widely used in metrology and industry in many areas of measurement and engineering.Delta check is the best tool currently available for detecting specimen misidentification.In calculating RCV, we first decided on the confidence interval. In this study, RCV was calculated using 99% confidence interval. The confidence interval of 95% for setting the delta check cutoff in the clinical laboratory tended to produce higher detection rate. The over‐detection tendency did not disappear even with the 99% confidence interval, suggesting that an additional modification was required for the RCV‐based delta check method.Among the additional modifications, there was a method to exclude the pair from the delta check alert if both values were within the reference interval. The rationale for coming up with this method is that the main purpose of the delta check is detecting the laboratory error, but it can be also used to detect the change in a patient's condition. This modification can increase the probability that the delta check alert has actual clinical significance, and this method has actually contributed to the reduction of over‐detection in analytes such as cholesterol and total bilirubin.The time factor is not reflected in the calculation formula of RCV. However, in the clinical laboratory, the rate which reflects time factor is often used in the delta check rule. In the case of an analyte that maintains a constant concentration in the body, such as sodium or chloride, the interval between test points has no significant impact on delta assessment. However, some enzymes, such as AST, are susceptible to false‐positive delta check alerts if changes over time are not considered, especially for outpatients since as the interval between two measurements is relatively longer than that of hospitalized patients, over‐detection occurs if the time factor is not reflected. Therefore, in the case of an analyte in which the time factor can play a critical role in the delta check, modification is necessary to reflect the time factor even if the cutoff is set using RCV.In the case of an analyte with a narrow reference interval and a small change in the test result, if a percent change is used for a delta check, a small change may appear as a large value and generate over‐detection. Because RCV is basically a percentage value, universal RCV application cannot account for these characteristics of all the test items. Therefore, in this study, by using a formula multiplied by RCV and twice the upper limit value of the reference interval, we attempted to convert the RCV value, which is a percentage, to compare with the absolute difference, and we observed that it was of great help in reducing over‐detection.On the other hand, in the current delta check rule used by our laboratory, positive delta and negative delta are set differently in analyte such as creatinine, AST, ALT, ALP, total bilirubin, BUN, and direct bilirubin. In Ko's study, the positive delta is larger than the negative delta in the analyte that reflects tissue damage and in the analyte that can rise due to a patient's condition.In glucose, the detection rate of 10% or less was not achieved in any of the delta check rules used in this study. This may be due to the clinical characteristics of the analyte. Glucose has a very large variation during a single day, depending on diet or blood sugar depressants or insulin administration. Therefore, it is difficult to regard a large change in glucose as a laboratory error such as specimen mix‐up. Of the total 46 institutions in the Q‐Probe study conducted by CAP, 16 used glucose as a delta check analyte and which is less than half of the total.In conclusion, we applied the delta check rule reflecting RCV to the actual clinical laboratory test results and proposed modified delta check rule, which corrected and supplemented the shortcomings of RCV only method. Clinical laboratory managers need to review the delta check that has been done on a daily basis comprehensively from applicable items to detailed application methods. It is necessary to understand the concept and basic principle of RCV and to use it to set delta check rule and cutoff to balance between the laboratory workload and the laboratory error detection.


We find that simple heuristic methods that select nodes with high degree or many contacts compare well in terms of outbreak detection performance with the (greedily) optimal set of nodes. Furthermore, we observe that nodes optimized on past periods may not be optimal for outbreak detection in future periods. However, seasonal effects may help in determining which past period generalizes well to some future period. Finally, we demonstrate that the detection performance depends on the simulation settings. In general, if we force the simulator to generate larger outbreaks, the detection performance will improve, as larger outbreaks tend to occur in the more connected part of the network where the top monitoring nodes are typically located. A natural progression of this work is to analyze how a representative set of outbreak scenarios can be generated, possibly taking into account more realistic propagation models.Epidemic spreading is a widely studied process due to its importance and possibly grave consequences for society. While the classical context of epidemic spreading refers to pathogens transmitted among humans or animals, it is straightforward to apply similar ideas to the spread of information  or the spread of computer viruses. This paper addresses the question of how to optimally select nodes for monitoring in a network of timestamped contact events between individuals. We consider three optimization objectives: the detection likelihood, the time until detection, and the population that is affected by an outbreak. The optimization approach we use is based on a simple greedy approach and has been proposed in a seminal paper focusing on information spreading and water contamination. We extend this work to the setting of disease spreading and present its application with two example networks: a timestamped network of sexual contacts and a network of animal transports between farms. We apply the optimization procedure to a large set of outbreak scenarios that we generate with a  Spreading processes in networks have been extensively studied in the literature. Spreading can happen in a wide variety of contexts, including animal or human epidemic spreading  propagation model  we simulate a large set of possible outbreak scenarios according to a simple The present study has two main objectives. First, we aim to investigate the characteristics of the set of nodes selected according to the greedy optimization approach proposed by Leskovec et al. , as compGreedy optimization of submodular set functions goes back to the seminal work by Nemhauser et al. who provide “worst case bounds on the quality of the [greedy] approximations.” Nemhauser et al. . More cor et al. . A populr et al.  show thar et al.  adopt a r et al. . Anotherr et al.  who suggr et al.  examine i is larger than all other nodes’ marginal gains from the previous iteration, submodularity implies that i will also exhibit the largest marginal gain in the current iteration. This can dramatically reduce the number of function evaluations. However, Leskovec et al.’s approach has a limited applicability in temporal networks. For example, they show that the optimal node set selected based on observed blog network information cascades in the past does not generalize well to future outbreak detection. The reason for this is that blog networks are inherently dynamic and may change over time, such that the optimal node set will change substantially over time. One of the main objectives of this article is to assess this generalization problem in the context of epidemic spreading on a sexual contact network and an animal transport network. As mentioned above, the computationally expensive simulations may impair their use on large or very dense networks. However, there have been recent contributions in this area that substantially improve the efficiency of spreading simulations on temporal networks. For example, Vestergaard and Génois . Leskovec et al.  demonstrd Génois  propose d Génois , St-Onged Génois , Holme  exhibit low uncertainty about the origin of the outbreak. However, this approach assumes that the starting time of the outbreak is known and spreading cascades are only simulated from this one starting time. Assuming that the specific contact patterns that underlie an emerging spreading process are not available, Valdano et al.  and E the set of edges . The edges in the network are timestamped. Accordingly, an edge can be represented as a triple t a timestamp, indicating when the contact took place. The network can be directed or undirected. We assume that a single node introduced a disease into the network. The disease is then propagated along the edges in accordance with a propagation model, which is assumed to be known. Here, we use the classical susceptible-infectious-recovered (SIR) model: nodes get infected with probability k on the number of nodes we can monitor. Thus, the optimization problem (for the DL objective) is the following:minimized. Note that all three objectives are set functions that take a set of nodes Let information about contacts between individuals be organized as a network c et al. : the detThe outbreak detection approach used here is to a large extent identical with the one proposed in Leskovec et al. . Howeverpenalty reductions. The benefit of that is based on the fact that many nodes will have a penalty reduction of 0, which makes it a sparse optimization problem. The set of simulated outbreak scenarios can be denoted by i being one specific outbreak scenario. The penalty that scenario i incurs depends on the time it is detected by the monitoring set i as i. Note that the penalty will assume some maximal value if a scenario is not detected. We can write this maximal penalty as T and the start of the outbreak i as i, and 0 otherwise. Figure Leskovec et al.  propose mentclass2pt{minimweighted penalty reduction (PR) for a given set of nodes i have the same weight The As mentioned before, we do not usually observe exact disease infection cascades. Hence, we need to simulate a large set The simulation results are organized as an inverted index as suggested by Leskovec et al. . For evek becomes extremely large even for small graphs , selecting high-degree nodes for monitoring seems to be a good strategy for efficient outbreak detection Holme . Note thstrength of a node . The most active sex-seller is active 424 times during this 3-year period, while the most active sex-buyer totals 128 contacts in the same period.In the first example, we consider the spread of sexually transmitted infections (STIs). We use a dataset of timestamped sexual contacts between sex workers and their clients, which is widely used in research on temporal contact networks . Hence, for any monitoring set rPR, as the fraction of the current penalty reduction and the maximal possible penalty reduction, that is,We greedily optimize the set of monitoring nodes for all three objectives on a year’s worth of data and call this the training phase. For this training phase, we simulate 5 million outbreak scenarios. As mentioned earlier, an outbreak is defined as a spreading process including at least one transmission of the disease between any pair of nodes. This is equivalent to a minimal outbreak size of 2. For the testing phase, we generate another 10, 000 outbreak scenarios  either on the same period as for the training phase or on another period. In this subsection, we will train and test on the same period (year 3) and compare the relative performance of all three objectives. Results for other years are similar. We set In Fig. k. We can see that, especially in the beginning, the PA and the DT objective have a higher overlap with the degree heuristic than the DL objective. For Panel (a) of Fig. All previous results use the same period for the training and the testing phase. However, in a realistic setting we will have to optimize the set of nodes on past data and hope that the monitoring set will also perform well on future data. We presume that the generalization to future data will be better if the network exhibits some regularities or some backbone that does not change much over time. Panels (a), (b), and (c) in Fig. The results thus far are based on outbreak simulations that require at least one transmission of the disease. We can parameterize this by defining a minimal outbreak size (MOS). The higher the MOS, the more we bias the set of outbreak scenarios towards large outbreaks. For a minimal outbreak size of 1, the average outbreak size of the 10, 000 simulated outbreaks during the testing phase is 1.65 and the maximal outbreak affects 207 nodes. For a minimal outbreak size of 2 (this corresponds to the setting that we have considered so far), the average outbreak size is 10.75 and the maximal outbreak size is 313. Finally, for a minimal outbreak size of 5, the average outbreak size is 25.88 and the maximal outbreak size is 347. It is obvious that the performance of the outbreak detection methods is strongly affected by the way we simulate the outbreak scenarios. If we bias them towards larger outbreaks, then the detection performance will be better as large outbreaks occur in the more connected part of the network where the highly central nodes will catch many of them. This is illustrated in Panels (a)–(c) in Fig. In the second example, we consider reported pig movements in Switzerland during the year 2017 , we only simulate 1 million outbreak scenarios for the training phase. As before, we focus on a minimal outbreak size of 2, i.e., there must be at least one transmission of the pathogen from one animal holding to another. For the testing phase, we again simulate 10, 000 outbreak scenarios and we only present results for the top 200 nodes. Panels (a)–(c) in Fig. If we compare the greedy optimization method with the heuristic benchmarks for the three objectives, we get an interesting result. For the DL and DT objective (Panel (a)–(b) in Fig. Panel (a)–(c) in Fig. Since we use monthly networks in this example, we use 12 networks (January–December) for the training phase and test the results on outbreaks simulated on the December network. Panels (a)–(c) in Fig. t and month Panel (b) in Fig. We have noted above that the way we simulate the outbreak scenarios can strongly impact the performance of the different outbreak detection objectives. Here, we also use different minimal outbreak sizes (MOS). If Panels (a)–(c) in Fig. The aim of this paper is to extend the greedy optimization method for outbreak detection  or African swine fever (ASF). In line with the related work  are different from nodes satisfying the DL and DT objective (large in-degree). This may have to do with the structure of the Swiss livestock industry, which is organized in a hierarchical and decentralized way where farms specialize on a specific task in the production process  outbreak scenarios is not as straightforward as it seems. We introduced a minimal outbreak size in order to set a lower bound for the size of an outbreak. However, this results in a heavily skewed distribution of outbreak sizes as a large portion of the outbreak scenarios will exhibit the minimal outbreak size. By increasing the minimal outbreak size we can force the simulation approach to generate larger outbreaks, which will improve the detection performance as demonstrated in this paper. This implies that the detection performance of the greedy approach and the heuristics is strongly affected by the way we simulate outbreaks (mostly in terms of size). Future work on defining and creating Taken together, the findings of this study suggest that the optimal nodes for outbreak detection are often the highly central nodes in the network because they are either infected early on or they are the origin of a potentially large outbreak themselves. Furthermore, the question which past period should be used to select nodes for future outbreak detection depends on the degree of organization a system exhibits but also on seasonal patterns. The findings of our paper may be somewhat limited by the fact that we only consider two example networks and one specific spreading process, namely a SIR model. To develop a full picture of the greedy optimization approach for outbreak detection and its comparison to heuristics, additional studies are needed that examine its performance over a range of parameter values and for different network structures and spreading models.


In the design of intervention and observational epidemiological studies sample size calculations are used to provide estimates of the minimum number of observations that need to be made to ensure that the stated objectives of a study are met. Justification of the number of subjects enrolled into a study and details of the assumptions and methodologies used to derive sample size estimates are now a mandatory component of grant application processes by funding agencies. Studies with insufficient numbers of study subjects run the risk of failing to identify differences among treatment or exposure groups when differences do, in fact, exist. Selection of a number of study subjects greater than that actually required results in a wastage of time and resources. In contrast to human epidemiological research, individual study subjects in a veterinary setting are almost always aggregated into hierarchical groups and, for this reason, sample size estimates calculated using formulae that assume data independence are not appropriate. This paper provides an overview of the reasons researchers might need to calculate an appropriate sample size in veterinary epidemiology and a summary of sample size calculation methods. Two approaches are presented for dealing with lack of data independence when calculating sample sizes: (1) inflation of crude sample size estimates using a design effect; and (2) simulation-based methods. The advantage of simulation methods is that appropriate sample sizes can be estimated for complex study designs for which formula-based methods are not available. A description of the methodological approach for simulation is described and a worked example provided. In the design of intervention and observational epidemiological studies sample size calculations are used to provide estimates of the minimum number of observations that need to be made to ensure that the stated objectives of a study are met , 2. PeerMethods for sample size estimation vary depending on the type of study being carried out i.e., observational  or interventional . Formula-based approaches for sample size estimation are often preferred by investigators because: (1) they are relatively quick and simple to implement; (2) their widespread use makes peer review challenge less likely; and (3) the ability to use standard formulae goes hand in hand with “standard” study designs . Use of a standard study design implies the use of established approaches for data collection and analysis, again reducing the likelihood of challenge during peer review. In veterinary epidemiology the aggregation of animals into often several levels of hierarchy  complicates sample size calculations due to lack of data independence arising from study subjects being aggregated into groups . While modifications to standard sample size formulae are available, their flexibility to handle the range of real-world data situations is often limited.The aim of this paper is to provide an overview of sample size estimation methods and their usage in applied veterinary epidemiological research. The structure of the paper is as follows. In the first section an overview of formula-based approaches for sample size estimation in epidemiological research is provided. In the second section, formula-based approaches for calculation of appropriate samples sizes for clustered data are presented. In the third and final section simulation-based approaches are presented as a means for estimating an appropriate sample size for hierarchical study designs for which formula-based methods are not available. Examples are provided throughout the paper to illustrate and support the concepts discussed. The supplementary material contains code allowing readers to reproduce the results presented in each of the examples using functions available in the contributed epiR package  in R 6)6).In veterinary epidemiology sample size calculations are used during the design phase of a study to allow investigators to: (1) estimate a population parameter ; (2) test a hypothesis in an observational setting ; (3) test a hypothesis in an intervention setting ; and (4) achieve a specified level of confidence that an event will be detected if it is present at a specified design prevalence.P equals P × [1 − P]  is greatest when P = 0.5 and the absolute tolerable error and level of confidence remains fixed  545 cattle should be sampled.With stratified sampling the sampling frame is divided into groups (strata) and a random sample is taken from each stratum. When the variation of the outcome of interest within each stratum is small relative to the variation between strata, stratified random sampling returns a more precise estimate of the population parameter compared with simple random sampling.Details of the formula-based methods to estimate a sample size for each of the main observational study  are provided in Box 2nA ≥ 1040      A total of 2,080 male cats need to be sampled to meet the requirements of the study .In contrast to sample size formulae for cross-sectional and cohort studies, the sample size formula for case-control studies requires provision of an estimate of the prevalence of exposure amongst controls . An addiBox 3n ≥ 282     If the true odds for squamous cell carcinoma in exposed subjects relative to unexposed subjects is 2.0, we will need to enrol 141 cases and 141 controls  to reject the null hypothesis that the odds ratio equals one with probability (power) 0.80. The Type I error probability associated with this test of this null hypothesis is 0.05.A superiority trial is a study in which the aim is to show that a treatment intervention provides a better therapeutic outcome than a known reference (often a placebo) and the statistical procedure to provide this evidence is called a superiority test .In situations where an established treatment already exists a study comparing a new treatment to a placebo  will be unethical. In this situation interest lies in determining if the new treatment is: (1) either the same as, or better than, an established treatment using a non-inferiority trial; or (2) equivalent to an existing treatment within a specified range, using an equivalence trial . EquivalSuperiority, non-inferiority and equivalence trials require the analyst to specify an equivalence margin. The equivalence margin is the range of values for which the treatment efficacies are close enough to be considered the same . ExpressEquivalence margins can be set on the basis of a clinical estimation of a minimally important effect. This approach is subjective and, as a result, it is possible to set the equivalence margin to be greater than the effect of the established treatment, which could lead to potentially harmful treatments classified as non-inferior. A second approach is to select an equivalence margin with reference to the effect of the established treatment in trials where a placebo has been used. When the equivalence margin is chosen in this way, there is some objective basis on which to claim that a positive non-inferiority trial implies that a new treatment is, in fact, superior to the established treatment . An example sample size calculation for a non-inferiority trial is presented in Box 4nB = 25       A total of 50 subjects need to be enrolled in the trial, 25 in the treatment group and 25 in the control group.Sampling of individuals to either detect the presence of an event  or provide evidence that disease is absent from a jurisdiction are frequent activities in veterinary epidemiology. Typical scenarios include: (1) shipment of live animals from one country to another where the country receiving the shipment might request that testing is carried out on a sample of individuals, as opposed to testing every animal; and (2) a country wishing to re-gain official disease freedom status following an infectious disease outbreak.Details of formula-based sample size estimation methods to detect the presence of an event are provided in n individuals that return a positive test result. This allows an analyst to make a statement that they can be (for example) 95% confident that the prevalence of disease in the population of interest is less than the stated design prevalence if the number of (surveillance) units with a positive test result is less than or equal to a specified cut-point. A worked example of this approach is provided in the When tests with both imperfect diagnostic sensitivity and specificity are used, diseased individuals can be missed because of imperfect diagnostic sensitivity but at the same time disease negative individuals can be incorrectly identified as disease positive because of imperfect specificity. Cameron and Baldock  describeAggregation of individual sampling units into groups (“clusters”) for example farms, households or villages violates the assumption of independence that is central to the sample size calculation methods described so far. When individuals are aggregated into clusters there are two sources of variation in the outcome of interest. The first arises from the effect of the cluster; the second from the effect of the individual. This means that individuals selected from the same cluster are more likely to be similar compared with those sampled from the general population . For thiWith one-stage cluster sampling a random sample of clusters is selected first and then all individual listing units within each cluster are selected for study. With two-stage cluster sampling a random sample of clusters is selected first and then a random sample of individual listing units within each cluster is selected. The primary advantage of cluster sampling is logistics. In animal health, where animals are typically managed within clusters  it is easier to select clusters first and then, from each selected cluster, take a sample of individual animals. This contrasts with a simple random sampling approach which would require an investigator to travel to a large number of herds-flocks, sampling small numbers of animals from each. As explained above, the main disadvantage of cluster sampling is a reduction in the effective sample size due to animals from the same cluster being more homogenous (similar) compared with those from different clusters.D) to achieve the level of statistical power achieved using independent sampling. For a single level of clustering  the design effect is calculated as:To compensate for this lack of precision Donner et al.  proposedb equals the number of animals to be sampled from each cluster  and ρ is the intracluster correlation coefficient (ICC). The value of ρ equals the between-cluster variance In Equation 1 When there is little variation in an outcome within a cluster ρD = 1 + (75 − 1) × 0.20D = 15.8nc ≥ 96A total of 96 villages need to be sampled to meet the requirements of the study.The example shown in Box 6D = 1 + {(0.4672 + 1) 75 − 1}0.2    D = 19.1                nc ≥ 115    A total of 115 villages need to be sampled to meet the requirements of the study.An example showing how a crude sample size estimate  can be adjusted to account for clustering using the design effect is provided in Box 7D = 1 + (b − 1)ρD = 1 + (20 − 1) × 0.09D = 2.71After accounting for the presence of clustering at the herd level we estimate that a total of (545 × 2.71) = 1,477 cattle need to be sampled to meet the requirements of the survey. If 20 cows are sampled per herd this means that a total of  = 74 herds are required.Three levels of clustering are relatively common in veterinary epidemiological research (much more so than in human epidemiology) where, for example, lactations (level 1 units) might be sampled within cows (level 2 units) which are then sampled within herds (level 3 units). The total variance in this situation is made up of the variance associated with lactations within cows within herds and lactations within cows:In a study comprised of three levels the required sample size, accounting for clustering equals :m is the number of lactations to be sampled to meet the requirements of the study assuming the data are completely independent and n3, n2, and n1 are the number of units to be sampled at the herd, cow and lactation level (respectively). The design effect for three levels of clustering equals:In Equation 5, Box 8From  the stanand the ICC for lactations within cows (Equation 4):The mean and standard deviation of the logarithm of calving to conception interval was 4.59 and 0.54, respectively. What is the required sample size assuming the data are independent?Assuming the data are independent a total of 22 lactations are required to be 95% confident that our estimate of the logarithm of calving to conception interval is within 5% of the true population value.We elect to sample two lactations per cow. How many lactations are required to account for clustering of lactations within cows?A total of 25 lactations are required accounting for clustering of lactations within cows. How many cows are required?A total of 13 cows are required if we sample two lactations per cow .We now consider clustering at the herd level. How many lactations are required to account for clustering of cows within herds?Accounting for clustering of lactations within cow and cows within herds, a total of 39 lactations are required. How many herds are required?A total of 2 herds are required if we sample 13 cows from each herd and 2 lactations from each cow. The total number of lactations required is therefore:To account for lack of independence in the data arising from clustering of lactations within cows and cows within herds 52 lactations (2 lactations from 13 cows from 2 herds) are required to meet the requirements of the study.The required sample size assuming the data were independent was 22. The required sample size accounting for lack of independence in the data was 52, a 2.5-fold difference.In applied veterinary epidemiological research it is common for study designs not to conform to the standard study designs for which sample size formulae are available. Typical examples include situations where study subjects are organized into more than three levels of aggregation and in clinical trials where a treatment might be applied at the group level and a second treatment applied at the individual level. Where there are multiple levels of aggregation researchers may elect to apply a more conservative design effect multiplier than that used when study subjects are kept in simpler cluster groups. While this approach is an attempt to address the problem, it can result in the final sample size estimate being larger than the final sample size required if the design effect was known more precisely.For complex study designs simulation-based approaches provide an alternative for sample size estimation that is relatively easy to implement using modern statistical software . In the n units sampled that returns an estimate of the outcome variable that is within the prescribed relative error of the true population value on 95% of occasions. Note that several different combinations of units sampled at each level might achieve the stated objectives of the study.The general approach when using simulation to estimate a sample size to estimate a population parameter is to: (1) simulate a population data set that respects clustering of the outcome variable within the population of interest; (2) define a series of candidate sample size estimates; (3) repeatedly sample the simulated population using each of the candidate sample size estimates to determine the proportion of occasions the estimate of the population parameter is within the prescribed relative error of the true population value. When estimating a population prevalence and assuming the level of confidence specified by the analyst has been set to 95%, the required sample size is the combination of level 1, 2, 3, . When the study aim is to test a hypothesis, an additional step is to assign the exposure variable  to members of the population and then to estimate the effect of the exposure on the outcome of interest using a regression approach. Arnold et al.  provide n units in the population; (3) defining the level 1, 2, 3, ... n variance terms; (4) simulating a population of individuals eligible for sampling based on the specified number of level 1, 2, 3, ... n units; (5) assignment of a value for the outcome variable to each member of the simulated population, and; (6) adjustment of the value of the outcome variable for each individual to account for clustering using the level 1, 2, 3, ... n variance terms.Generation of the population data set involves: (1) defining the mean and standard deviation of the outcome of interest (for continuous outcomes) or the expected population prevalence (for binary outcomes); (2) defining the number of level 1, 2, 3, ... Code written in the R programming language (6) to generate a population data set for the Reunion Island dairy cow reproduction example (25) and estimate a sample size to meet the requirements of the study is provided in the Supplementary material accompanying this paper.n = 52 lactations) calculated using the formula-based approach presented in n = 6) were sampled from a larger number of herds .In summary, the process of simulation replaces the time and effort to derive a formula-based approach for a complex study design with basic programming and computer simulation time. An additional positive side effect is that the process of simulation requires investigators to define the structure of their study population, the expected value and variability of the outcome of interest and how the results of the study will be analyzed once the data are collected. This reduces the likelihood of investigators exploring alternative analytical approaches in the presence of negative findings, consistent with CONSORT guidelines .This paper has provided an overview of the reasons researchers might need to calculate an appropriate sample size in veterinary epidemiology and a summary of different sample size calculation methods. In contrast to human epidemiology individual study subjects in veterinary epidemiology are almost always aggregated into hierarchical groups  and, forThe author confirms being the sole contributor of this work and has approved it for publication.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


This article contains the data set and model code for the negative emission polygeneration system described in Tan et al. (2019). The data was generated utilizing an optimization model implemented in LINGO 18.0 and includes information on the operating state of each process unit in the system. The maximum annual profit of the system was determined at different carbon footprint targets. The data set and model code can be utilized for further analysis on the interdependence between the process units of this polygeneration system, its operational and environmental performance, and the potential impact of integrating new process units into the network. The code can be found in the supplementary file. The annual profit is maximized in consideration of 11 different scenarios, which varied only in the target carbon footprint. Furthermore, it is assumed that electricity price varies per hour; the EGDA variable cost is € 350/kW; the price of treated brine is € 0/t (corresponding to no price for CO2 captured); and that the price of HCl is € 80/t. The first scenario maximizes the annual profit with the maximum target carbon footprint of −4671.95 t/y, which is the lowest possible carbon footprint that can be achieved under these conditions. The last scenario maximizes the annual profit with a maximum target carbon footprint of 7585.32 t/y, which corresponds to the highest possible carbon footprint achieved under the given conditions. Scenarios 2 to 10 maximized the annual profit under different carbon footprint targets which have been generated by dividing the CO2 range into 10 increments. The summary of the trade-off between annual profit and carbon footprint is summarized in The data is used to identify the optimal operating state of the NEPS using the mixed-integer linear optimization model described in Tan et al.  and exec


In this report, the authors present the case of a child who sustained a specific type of mallet finger injury known as a Seymour fracture. This is an important injury to recognize in the emergency department as it is associated with significant morbidity if not treated appropriately. This is especially of concern in children, where the tissue can get trapped in the growth plate. Children also face a high risk of deformity due to growth arrest. Management includes thorough washout, reduction of displacement, and antibiotics and tetanus prophylaxis if there is an open fracture. Pediatric hand fractures make up 2.3% of all ED visits ; these aA mallet finger injury is one of the most common injuries and is frequently associated with sport-related activities. One specific type of mallet finger injury, the Seymour fracture, results from a juxta epiphyseal fracture on the distal phalanx of the finger . This inEarly evaluation and appropriate treatment of a Seymour fracture include the administration of tetanus and antibiotic prophylaxis, and prompt orthopedic consultation . SeymourA school-aged boy presented to the emergency department after catching his middle finger of his left hand in a hoverboard, which had caused avulsion of the distal phalanx through the skin. The bone was seen protruding through the bleeding, injured nailbed Figure .His vital signs included temperature of 98.1 °F, pulse of 98 beats per minute, respiratory rate of 16 breaths per minute, blood pressure of 117/73 mmHg, and oxygen saturation at 100% on room air. Physical examination revealed nail avulsion and ecchymosis. The sensation in the hand was intact. The radial pulse was also intact. The range of motion of the other fingers was also found to be intact. No laboratory studies were obtained. Radiographs demonstrated a severely displaced fracture through the epiphyseal plate of the base of the distal phalanx with a marked transverse displacement of the distal phalanx, with small soft tissue gas noted at the fracture site Figure .The wound was thoroughly irrigated with 250 ml of normal saline. No evidence of a foreign body was found. The patient’s tetanus status was up to date; he was given a weight-based dose of liquid ibuprofen for analgesia and one gram of cefazolin for the open fracture. The patient was transferred to the hand surgeon at the local children’s hospital due to the degree of displacement and the need for a thorough washout and closed reduction of the fracture.A Seymour fracture can easily be overlooked as a minor injury to the nail. The nail bed laceration itself is usually not visible, but the proximal edge of the nail plate sits on top of the eponychial fold rather than beneath, making the nail appear “too long” in comparison to the other nails .The pathophysiology, being similar to that of a mallet injury in adults, most commonly involves crushing injuries, sports injuries, and falls. This injury typically occurs when the distal phalanx of a fully extended digit undergoes forceful flexion, or when the distal phalanx experiences a crush injury . Early eTreatment for a Seymour fracture is dependent on whether it is an open or closed fracture. In the case of an open fracture that is prone to infection, it should be treated with operative irrigation, debridement, and intravenous antibiotics, along with a careful exploration to remove the proximal nail plate from the site of incarceration -6. In clSeymour fractures are rare and often underestimated injuries that require early treatment. This case presentation highlights the common features of a Seymour fracture. The common pitfalls associated with this fracture are as follows: 1) clinicians tend to minimize workup in pediatric patients and 2) proximal nail injury can look deceptively benign.