Telogen effluvium (TE) is a common form of hair loss characterized by diffuse hair shedding, resulting from the early entry of the hair into the telogen phase. Inducing factors include systemic diseases, stressful events, drugs, nutritional deficiencies, and major surgery. Hair loss occurs 3 months after the causing event and is usually self-limiting, lasting for about 6 months (acute TE). A chronic form of TE also exists, when the duration of hair loss exceeds 6 months. 1,2 Patients with TE, usually women, are often deeply anxious, reporting not sleeping or waking up in the night with their hair as their first thought. The disorder is as frequent and frightening as to make the patient urgently go to the dermatologist. TE may have a profound impact on the patients' mind and would require attention, time, and empathy. 3

Key grouping is a technique used by stream processing frameworks to simplify the development of parallel stateful operators. Through key grouping a stream of tuples is partitioned in several disjoint sub-streams depending on the values contained in the tuples themselves. Each operator instance target of one sub-stream is guaranteed to receive all the tuples containing a specific key value. A common solution to implement key grouping is through hash functions that, however, are known to cause load imbalances on the target operator instances when the input data stream is characterized by a skewed value distribution. In this paper we present DKG, a novel approach to key grouping that provides near-optimal load distribution for input streams with skewed value distribution. DKG starts from the simple observation that with such inputs the load balance is strongly driven by the most frequent values; it identifies such values and explicitly maps them to sub-streams together with groups of less frequent items to achieve a near-optimal load balance. We provide theoretical approximation bounds for the quality of the mapping derived by DKG and show, through both simulations and a running prototype, its impact on stream processing applications.

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This study suggests that the prognosis of MF patients is not only correlated with clinical/pathological/serological/immunological variables but it also relies on specific HLA alleles.