In addition to the gross IRR, firms also are required to present a measure of net returns after deducting carried interest and investment management fees from the gross return. The type of expenses included in the IRR calculation and the timing of cash flows related to expenses may have a significant effect on returns, and is an area to monitor for GIPS compliance.
The concept of fair value used in GIPS for private equity funds mirrors the fair value principles under U. Accordingly, investments are required to be reported at fair value consistently with the fair value reported in U.
GAAP financial statements. The benchmark chosen to compare performance of the composite must reflect the same vintage year used for the composite. Firms may elect not to present a benchmark if the investment managers believe that a benchmark is not available or appropriate for a specific composite.
In this case, GIPS requires the reason for not including a benchmark be disclosed in the composite presentation. GIPS requires private equity funds to provide additional information on their equity structure, capital activity and the character of their gains and losses.
They also have to report a number of additional metrics that include the total-value-to-since-inception-paid-in-capital ratio, the residual-value-to-paid-in-capital ratio, the distributions-to-paid-in-capital ratio and the paid-in-capital multiple, a ratio of the total paid-in capital to the cumulative committed capital.
Typically, the presentation of these additional disclosures would not represent a significant challenge for funds that already prepare U.
To be in compliance, firms that have been in existence for fewer than five years are required to report GIPS-compliant performance since inception. Firms that have been in operation for five years or more are required to show a minimum of five years of GIPS-compliant performance records in their initial GIPS-compliant reports. Firms are then required to add the GIPS compliant record of each subsequent annual performance, until they reach a year record, which can then be maintained on a rolling basis.
Firms may report more than the minimum GIPS-compliant track record if desired. GIPS compliance offers real advantages in a market where investors are seeking increased transparency and want more confidence in the reported performance of the funds they are considering. By understanding the steps above, private equity funds can better manage their GIPS compliance efforts.
To discuss how our team can help your business, contact us by phone Events and Webcasts. Security and Privacy. The member firms of RSM International collaborate to provide services to global clients, but are separate and distinct legal entities that cannot obligate each other. Replacement of deficient lymphocytes in mice resolved the learning and memory difficulties [ 89 ].
Treatment of allergies often results in improvement in autistic behaviors such as hyperactivity and irritability [ 66 ]. An early study found that treatment with intravenous immune globulin in ten children with autism resulted in better speech, eye contact, focus and awareness of surroundings [ 93 ]. Incidence of gastrointestinal GI disease among those with autism varies widely, depending on exclusion criteria and whether the study was prospective or retrospective.
These symptoms include abdominal pain, chronic diarrhea and or constipation, and gastro esophageal reflux disease [ 10 ]. GI disease has been confirmed via endoscopy in several studies [ 95 - 97 ]. Inflammation was found throughout the GI tract, with reflux esophogitis, stomach inflammation, duodenum and abnormal carbohydrate digestive enzyme activity.
Other studies have found chronic patchy inflammation and lymphonodular hyperplasia. This is different than the pattern seen in classical inflammatory bowel disease, with infiltration of T cells and plasma cells into the epithelial layers of the mucosa [ 68 , 97 ].
Lymphocyte infiltration into the epithelial layers of the gut lining and crypt cells has been found on endoscopy. In addition, there were IgG antibodies deposited onto the epithelium and complement immune system activation. This might be indicative of an autoimmune process [ 98 ]. There is evidence of increased intestinal permeability in people with autism [ 99 - ]. Intestinal permeability allows larger molecules that would normally stay in the gut to cross into the bloodstream.
Plasma and urinary concentrations of oxalate were greatly elevated in children with autism, which may be a result of increased intestinal absorption [ ]. Increased permeability can lead to allergy and autoimmune processes. There appear to be multiple reasons for the increased permeability. The dietary protein gluten can bind to the CXCR3 receptor, resulting in increased zonulin levels. Zonulin regulates the opening of the tight junctions in the gut [ ].
Ingested toxins such as Polychlorinated Biphenyls can also open the tight junctions in the gut [ ]. Increased incidence of dysbiosis, an imbalance of intestinal flora, has been noted in children with autism [ 99 , ] Dysbiosis can result from use of antibiotics. As beneficial bacteria are killed, antibiotic resistant pathogenic organisms can take their place.
It has been theorized that toxins produced by pathogenic organisms may be affecting the brains of individuals with autism. In addition, decreased levels of disaccharide digestive enzymes have been noted in children with autism [ 99 ]. There are anecdotal reports of improvement of autistic behavior on restricted diets. They found greater improvent in autistic behaviors in children with gastrointestinal symptoms compared to those without [ ]. The reported improvements may be due to several reasons.
Removal of allergens may result in lessened autoimmune reactions [ 66 ]. Removal of gluten may reduce intestinal permeability [ , ]. Removal of dietary proteins for which there is insufficient enzymic activity may reduce dysbiosis [ ].
The brain has the potential to directly effect the functioning of the gut. Stress has been implicated in Irritable Bowel Syndrome with alterations of the intestinal barrier function, altered balance in enteric microflora, exaggerated stress response and visceral hypersensitivity [ ]. Antidepressants [ ] and therapy [ ] have been found to be effective treatments for irritable bowel syndrome IBS and inflammatory bowel disease IBD. There is also a finding that the brains of patients with IBS have increased hypothalamic gray matter compared with controls, though it is unknown whether the brain changes result from long term IBS or are preexisting [ ].
Among the body systems involved in autism is obviously the brain. Anatomical differences in the cerebellum and amygdala have been noted in multiple studies, and other regions have been inconsistently identified as diverging from the average [ ]. Decreases in Purkinje and granular cells have been noted [ ]. The increase appears to be disproportionately from white matter enlargement. The cause of the macrocephaly is not known, though larger brains are prevalent among first degree, unaffected relatives.
Neuroinflamation is one postulated cause [ ]. Minicolumns in the neocortex have been postulated as the fundamental unit of cognition [ ]. Minicolumns in autistic brains appear to be narrower, with tighter spacing and higher neuron density [ ]. Whether this is a sign of pathology is unclear, as the same variation occurs in autopsies of three distinguished scientists [ ]. Autism does occur more often in families or mathematicians, engineers and physicists [ ].
It has been theorized that narrow minicolumns facilitate discrimination and more finely tuned activities, while wider minicolumns would facilitate generalization. This is consistent with the behavioral observations of stimulus overselectivity in autism. Stimulus overselectivity is the neglect of some features and the overly focused attention on other features, to the detriment of the observation of the whole [ ].
Functional MRI studies are giving evidence to enhanced local connectivity, and reduced global connectivity in the autistic brain. This might result in an over analysis of smaller features and an impairment in synthesizing the information into a coherent whole [ ].
It has been suggested that a feature in the development of autistic traits is a low signal to noise ratio in neural signals. In murine models, constant undifferentiated noise will indefinitely delay the maturation of neurons responsible for processing sound. A similar low signal to noise ratio in multiple systems in the autistic brain may be responsible for the impairments observed [ ]. This would be consistent with the underconnectivity theory.
Brain hypoperfusion has been noted in several studies of subjects with autism. Interestingly, the region affected can vary widely. Hypoperfusion can result from structural abnormalities or from global effects such as oxidative stress [ 7 ]. In addition, subclinical seizures are often present and treatment with anti-epileptics can result in mental improvement [ , ].
All of the systems described above interact in highly complex ways. To date, little research exists in autism modeling outside of the genetic and neurological systems. Finding commonalities between autism and other conditions may lead to new treatments. Rzhetsky used statistical models to find genetic overlaps between autism, bipolar disorder and schizophrenia [ ].
Individual subsystems of importance in autism have been modeled [ , ], but work needs to be done in modeling combinations of systems. It is clear that autism poses a challenging problem for modeling due to the high level of interactions between the different elements [ ].
This illustrates just one example of an intersystem interaction between the mitochondrial, immune and neurological systems. For example, food allergies or special diets would change the environment through different food choices. Fecal incontinence in older children would change the activities the child would be exposed to. Energy deficits from mitochondrial dysfunction could affect school activities. And being oversensitive to sensory input would change activities and family dynamics.
Much work has been done investigating the genetic basis of autism. Additional work needs to be done to find and cluster the genes involved in autism. Modeling autism will require an integration of both systems and scales. A few potential research areas are presented below.
Modeling autism is complex due to the different physiological scales involved. Issues of importance to model range from the organ level to the genetic. Outside of a few systems, such as the cardiovascular [ ], less work has been done on an organ scale. To create a true model of the human body, the microscopic and macroscopic need to be integrated. One way to do this is to use a hierarchical system. Modules can be developed to model the scale being considered, with appropriate links between levels.
Techniques have been borrowed from the systems engineering and software engineering communities to aid and formalize these connections between modules. An example is the BioUML, an open source platform for multilevel biology modeling [ ]. Hierarchical modeling using rule based models has been implemented at a cellular level [ ].
A hierarchical approach allows for separation of development of models for subsystems, but global effects of different substances and conditions need to be considered too. Studies of trans-organ and system effects of substances is a relatively unexplored field of study. For example, oxidative stress affects the mitochondria directly [ 24 ], but also the larger systems such as the brain [ 47 ]. Mitochondrial stress may also affect the brain indirectly.
Mitochondrial stress may lead to lipid peroxidation leading to reactive aldehyde generation in the liver, and finally to microglial activation and neuronal death [ 61 ]. Inflammation can affect many body systems. Inflammation can also be part of a feedback mechanism where inflammation creates conditions which create or perpetuate inflammation [ ]. Xenobiotic substances must be taken into account. Many exogenous substances are not typically included in existing models. Toxins such as PCBs, pesticides and heavy metals can affect the efficiency of enzymes often deficient in autism and need to be considered as a potential causative element [ 18 , 38 ].
In addition, the effect of toxins in combination may not be the same as the effect of the toxins in isolation [ - ]. The microbiome, the complex ecosystem of intestinal flora, may have an impact on many systems in the body either through immunological effects, or through the microbial metabolites such as the proprionic acid produced by clostridium [ ].
Special diets and supplements used by many on the autism spectrum may affect the composition of the microbiome in addition to possibly changing the function of enzymes [ , ]. The development of autism appears to be a complex interaction of genes and environmental factors. Since most cases of autism are idiopathic, there are an unknown number of subgroups that may be present.
Treating autism as homogeneous will obscure the differences required to ascertain the variances needed for proper treatment. Identification of subgroups would aid in both research and treatments.
This subtyping can be done on the basis of genes or clinical data. Clustering has been tried using behavioral symptoms but has had little success at identifying latent factors [ - ]. The benefits of subgrouping are as follows. Subgrouping the population might result in subgroups that have distinctive symptoms and pathology that are already familiar in the medical literature, and can draw upon treatments that work in existing treatable conditions.
For example, if one subgroup is a variant of a known syndrome, we can possibly benefit from the treatments known in the context of that syndrome. Subtyping would reduce the use of therapeutic trials, allowing a more targeted treatment. Another benefit that accrues from subgrouping is in prevention. If we know the sequelae of another similar condition, we can take appropriate action to include appropriate preventive measures in the treatment protocol.
For example, if seizures are a symptom of the similar known syndrome or condition, potentially a periodic EEG evaluation could be included in the treatment protocol. Biomarkers can be used for clustering subgroups. Many of the metabolic, immunologic, proteomic, genetic and anatomical differences listed above can be used to search for subgroups [ , ].
Biomarkers can also be identified with more advanced methods [ 16 , ]. An important consideration is that the biomarkers used be clinically relevant, chosen to maximize the potential for treatment [ ]. For example, the following parameters could be included in a feature vector in the subgroup calculation algorithm, for the purpose of clustering:. This can include genetic panels such as mitochondrial or results of microarray testing.
The treatments could include steps to address some of the disease markers discussed above, such as methycobalamin and folinic acid [ 36 ] for methylation issues and carnitine [ 61 ] for mitochondrial issues. The feature vector would be a vector with both specific values and binary numbers as markers such as a 1 for the presence of a polymorphism or other hard symptom and a 0 for none.
Once in numerical form, a variety of pattern recognition techniques can be used. One popular clustering technique is the K-means [ ]. The k-means algorithm is essentially a density finder. It assigns each input vector using an indicator function to a cluster defined by a prototype vector. The algorithm then minimizes the global average squared Euclidean distance from each input vector to the prototype. This optimization changes the position of the prototype vector to reflect Euclidean density patterns.
The prototype center is the average of the input vectors assigned to it and thus potentially representative of a subgroup. One weakness of the k-means is that it performs a hard assignment of each input vector to a cluster. An input vector is either entirely in a cluster or not at all. This would not match situations where there might be an overlap of symptoms. Fuzzy techniques would be of value in these cases. Fuzzy set theory allows intermediate levels, between 0 and 1, of belonging to member sets.
The fuzzy c-means FCM is a fuzzy generalization of the k-means algorithm to allow input vectors to belong to more than one prototype [ ]. The FCM also does not suffer from the stability problems that sometimes occur in the k-means when an input vector will switch back and forth between two prototypes, and thus changing the prototypes in the process.
An important issue with clustering algorithms is the number and validity of clusters. The k-means and FCM algorithms will find the number of clusters specified during program initialization, regardless of the actual number of clusters. Some clustering algorithms can produce clusters that are empty or degenerate. Many practitioners will heuristically try different numbers of clusters and asses the fit. There are also various methods to attempt to quantify the validity of clusters [ ]. The Self Organizing Map SOM maintains a proximity relationship between clusters and can be useful for visualization [ ].
The above techniques are unsupervised. Unsupervised techniques relay wholly on the input data to find clusters or groupings in the data. Supervised techniques incorporate additional knowledge about the expected groupings to guide the cluster development process. This additional information, if available, can aid in complex and high dimensional problems. Support Vector Machines [ , ] and a variety of Neural Network algorithms can be used to find patterns in the data [ ]. This ground truth can be information such as genes already associated with a phenotype or reaction to an intervention.
It could also be symptoms that could also be used as inputs, such as the before mentioned presence of epilepsy. Most of the algorithms mentioned above measure similarity based on the Euclidean distance metric.
Other distance measures are possible such as various correlation measures [ ] and non-spherical distance measures such as the Mahalanobis distance [ ]. Another issue is the scale of the data. Expected results in lab tests may vary by several orders of magnitude. Therefore, it is usually advisable to normalize the data before using in an algorithm.
The curse of dimensionality refers to the somewhat counterintuitive properties of high dimensional spaces whereby additional information can result in a lessening of discernment. The simplest of the implications of high dimensional space is that the amount of data required to adequately cover a volume increases exponentially with dimension.
It can be shown geometrically that most of the volume of a high dimensional Gaussian is contained in its tails, rather than at its center. This has obvious implications to distance based algorithms. The distance from a center of a cluster to any point is concentrated in a small interval and the relative differences from various data points to the prototype become essentially the same. Thus discriminatory power can decrease with added information, even if that additional information has discriminatory power in of itself [ , ].
That has implications for finding subgroups in a complicated disease such as autism that might require a large number of features. Feature selection will alleviate the curse of dimensionality but may exclude features needed to find less prevalent subgroups. The curse of dimensionality may also be avoided by using subspace methods or hierarchical clustering. Another issue prevalent in autism data is the abundance of missing data.
One cause of missing data would be different protocols for different studies resulting in similar but not identical feature vectors. When utilizing clinical data, physicians will not perform all tests on all patients, resulting in missing data when patients are combined.
Therefore techniques need to be utilized to make the most of the data that is present [ , ]. Numerical data in autism research has particular challenges. The data can refer to disparate body systems. Data can be problematic to integrate across studies and research centers. For example, studies can have different selection criteria, experimental conditions, and goals. Research centers can have different testing procedures which can lead to varying results. Data is often not precise.
Fuzzy techniques should be incorporated, as many of the data considered will not be easily quantifiable, such as parent reports of behavior. Also, what might be considered outlier data may in fact be important. It may be representative of the extreme values that are evident in autism data [ ]. There are a myriad of information that might be useful in determining autism phenotypes. As mentioned before, it might include items such as genotype information and lab results. It also might include items such as parent ratings of diarrhea odor.
Incorporating domain knowledge into the identification of subgroups will alleviate many of the problems noted above. As shown in the preceding sections, there is much qualitative information about autism contained in the medical literature. Most of it is single system studies.
Techniques need to be discovered to integrate this information together. One way to incorporate domain knowledge is to embed causal information into the solution [ ]. Some preliminary, simple subgrouping has already shown promise. An analysis of the gluten and casein elimination diet showed greater improvement in symptoms in children with gastrointestinal symptoms compared to those without [ ].
This information can help practitioners decide whether to recommend restrictive diets. It has been proposed that there may be a mitochondrial [ 58 ], intestinal permeability [ ] and immune subgroups [ 11 ] in autism, but it is probably more complex than that as many children may belong to multiple subgroups.
Thus it is imperative to develop subgroups that have clinical significance for treating the symptoms of autism, not just statistical validity. For example, one could, possibly discover a subtype of autism that presents with clinical or subclinical seizures of a certain characteristic type.
The treatment of seizures being a well-studied area, by itself, we could potentially establish a treatment protocol for patients in this subgroup, using treatment studies of drugs used for seizures in these patients also presenting with autism. This would result in a new treatment for those with autism, in contrast to using a seizure medication as an off-label drug without clear evidence of efficacy in this population. Another issue of importance is the time scales involved.
Autism is a developmental, not a static disease. Disease progression might start prenatally and extend throughout childhood. Modeling incorporating time progression has been primarily on the genetic or cellular level. Frameworks have been developed for parameter adjustments during phenotype transitions [ ]. Molecular connectivity maps incorporating differentially expressed genes have been used to investigate the relationship of aging to neurological and psychiatric diseases [ ].
Another time range to be considered is the progression through generations. Transgenerational changes have been shown with common toxicants. Low level bisphenol A exposure during pregnancy in mice resulted in transgenerational alterations in gene expression and behavior [ ].
She would then pass on a greater than normal amount of toxins to her child prenatally [ ] and through breast feeding [ ]. This will impair the detoxification systems of the child from an early age, resulting in an even greater build-up of toxins.
If this child, a girl, has children, she would pass on an even greater toxic load to her children. As the effects of toxins are more severe the earlier they are introduced, this might lead to developmental delays, including autism.
Thus a non-genetic, non-epigenetic trans-generational inheritance could be occurring. A recent study showed a three-fold rate increase of autism in the descendants of survivors of the mercury induced Pink disease infantile acrodynia. The study did not separate out matrilineal descendants, so it is impossible to determine whether there were toxins passed in utero, or whether the increased incidence was a result of a genetic hypersensitivity to mercury [ ].
This sort of inheritance can also happen in other systems [ ]. Inducement of diabetes in pregnant rats will result in increased prevalence of diabetes and obesity in the offspring. This can lead to gestational diabetes in the children and perpetuation of the diabetes through generations, through environmental causes [ , ]. Another source of time-dependence is that the brain itself is a state machine, in the sense that future characteristics depend on past characteristics, the various interventions employed or not employed at a certain time, etc.
Simplified modeling with reasonable assumptions can be potentially employed to answer questions of generic value. In summary, a time-dependent model will throw more light into brain plasticity and its contribution to the outcomes that we see in this population. In order to introduce this complexity, we propose enhancing our models using Dynamic Time Warping DTW [ ] or a more complex model with state information, similar to hidden Markov models where the body is assumed to be in a state where it produces certain symptoms or observations and transitions to other states based on the model.
Estimating these models and predicting outcomes would be the most complex of the techniques proposed in this article, and would be the goal for modeling such a complex time-varying system. This paper contains, of necessity, an incomplete review of the issues involved in autism. Research is exploding in this area and new findings are being published every month.
It is clear that the complexity of autism presents a both challenge and an opportunity for systems biologists. Modeling autism requires new techniques to be developed to harness and tame the complexity of interactions. Application of padding BK Pop Take up slack by making tucks in the plaster bandage The cast should be trimmed in line with the metatarsal heads on the plantar aspect and at the base of the toes dorsally. The fifth toe must be entirely free; this is a common site for a plaster sore.
Perkins stated that it is most important not to immobilize the forefoot in varus, and he left the metatarsal heads free to bear weight. If a toe plate is used, the metatarsophalangeal joints must not be held in hyperextension.
In fractures of the lower third of the tibia, dorsiflexion of the foot frequently causes angulation of the fracture. It is quite permissible under these circumstances to immobilize the foot in plantar flexion, although in fractures of the ankle this would be proscribed. When the foot is immobilized in plantar flexion and a walking heel is applied, the contra lateral shoe should be raised to equalize leg lengths.
Many orthopaedic surgeons reinforce their casts by applying splints to the posterior aspect of the cast. This adds weight without adding much strength. The same amount of plaster applied anteriorly as a fin strengthens the cast immeasurably, making fracture of the cast at the ankle virtually impossible. Avoid the use of hip and shoulder spicas in adults. They are more readily tolerated in children. Make sure there is someone at home during the day to care for the patient you discharge in a spica.
In the past, patients were frequently sent home in hip spicas to make space in hospitals. In their home environment, if no one cares for them, they may lie unturned, soaking in their own urine and feces and manufacturing immense decubitus ulcers.
We have seen a paraplegic woman with a fractured spine transported in a double hip spica, who on arrival had bone showing over both iliac spines, both greater trochanters, and her sacrum Spica Cast A one and a half spica cast. The leg portions are reinforced with a piece of wood.
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